[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-NiuTrans--ABigSurvey":3,"tool-NiuTrans--ABigSurvey":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",155373,2,"2026-04-14T11:34:08",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":76,"owner_email":77,"owner_twitter":76,"owner_website":76,"owner_url":78,"languages":76,"stars":79,"forks":80,"last_commit_at":81,"license":82,"difficulty_score":83,"env_os":84,"env_gpu":85,"env_ram":85,"env_deps":86,"category_tags":89,"github_topics":90,"view_count":32,"oss_zip_url":76,"oss_zip_packed_at":76,"status":17,"created_at":97,"updated_at":98,"faqs":99,"releases":129},7476,"NiuTrans\u002FABigSurvey","ABigSurvey","A collection of 1000+ survey papers on Natural Language Processing (NLP) and Machine Learning (ML).","ABigSurvey 是一个专为自然语言处理（NLP）和机器学习（ML）领域打造的综述论文宝库，汇集了超过 1000 篇高质量的学术调研文章。面对该领域技术迭代快、文献数量庞大且分散的痛点，ABigSurvey 通过系统化的整理，帮助从业者快速定位核心资料，避免在海量信息中迷失方向。\n\n该项目严格参照 ACL 和 ICML 等顶级会议的投稿指南，将文献精细划分为数十个热门主题，涵盖大语言模型、提示工程、机器翻译、深度学习架构、扩散模型及联邦学习等前沿方向。其独特亮点在于不仅提供了带有链接的完整论文清单，还对各类问题的研究热度进行了统计梳理，甚至专门发布了针对大语言模型（LLM）的独立综述列表，让用户能一目了然地把握学科发展脉络。\n\nABigSurvey 非常适合人工智能领域的研究人员、算法工程师、高校师生以及希望深入理解技术原理的开发者使用。无论是为了撰写文献综述、寻找研究灵感，还是想要快速入门某个细分赛道，它都能提供极具价值的指引，是探索 AI 知识版图的高效导航仪。"," # A Survey of Surveys (NLP & ML)\n\nIn this document, we survey hundreds of survey papers on Natural Language  Processing (NLP) and Machine Learning (ML). We categorize these papers into popular topics and do simple counting for some interesting problems. In addition, we show the list of the papers with urls (1063 papers). \n\n:new: A list of LLM surveys is released! [Link](https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurveyOfLLMs)\n\n## Categorization\n\nWe follow the ACL and ICML submission guideline of recent years, covering a broad range of areas in NLP and ML. The categorization is as follows:\n+ Natural Language Processing\n    + \u003Ca href=\"#computational-social-science-and-social-media\">Computational Social Science and Social Media\u003C\u002Fa>\n    + \u003Ca href=\"#dialogue-and-interactive-systems\">Dialogue and Interactive Systems\u003C\u002Fa>\n    + \u003Ca href=\"#generation\">Generation\u003C\u002Fa>\n    + \u003Ca href=\"#information-extraction\">Information Extraction\u003C\u002Fa>\n    + \u003Ca href=\"#information-retrieval-and-text-mining\">Information Retrieval and Text Mining\u003C\u002Fa>\n    + \u003Ca href=\"#interpretability-and-analysis-of-models-for-nLP\">Interpretability and Analysis of Models for NLP\u003C\u002Fa>\n    + \u003Ca href=\"#knowledge-graph\">Knowledge Graph\u003C\u002Fa>\n    + \u003Ca href=\"#language-grounding-to-vision-robotics-and-beyond\">Language Grounding to Vision, Robotics and Beyond\u003C\u002Fa>\n    + \u003Ca href=\"#large-language-models\">Large Language Models\u003C\u002Fa>\n    + \u003Ca href=\"#linguistic-theories-cognitive-modeling-and-psycholinguistics\">Linguistic Theories, Cognitive Modeling and Psycholinguistics\u003C\u002Fa>\n    + \u003Ca href=\"#machine-learning-for-nlp\">Machine Learning for NLP\u003C\u002Fa>\n    + \u003Ca href=\"#machine-translation\">Machine Translation\u003C\u002Fa>\n    + \u003Ca href=\"#named-entity-recognition\">Named Entity Recognition\u003C\u002Fa>\n    + \u003Ca href=\"#natural-language-inference\">Natural Language Inference\u003C\u002Fa>\n    + \u003Ca href=\"#natural-language-processing\">Natural Language Processing\u003C\u002Fa>\n    + \u003Ca href=\"#nlp-applications\">NLP Applications\u003C\u002Fa>\n    + \u003Ca href=\"#pre-trained-models\">Pre-trained Models\u003C\u002Fa>\n    + \u003Ca href=\"#prompt\">Prompt\u003C\u002Fa>\n    + \u003Ca href=\"#question-answering\">Question Answering\u003C\u002Fa>\n    + \u003Ca href=\"#reading-comprehension\">Reading Comprehension\u003C\u002Fa>\n    + \u003Ca href=\"#recommender-systems\">Recommender Systems\u003C\u002Fa>\n    + \u003Ca href=\"#resources-and-evaluation\">Resources and Evaluation\u003C\u002Fa>\n    + \u003Ca href=\"#semantics\">Semantics\u003C\u002Fa>\n    + \u003Ca href=\"#sentiment-analysis-stylistic-analysis-and-argument-mining\">Sentiment Analysis, Stylistic Analysis and Argument Mining\u003C\u002Fa>\n    + \u003Ca href=\"#speech-and-multimodality\">Speech and Multimodality\u003C\u002Fa>\n    + \u003Ca href=\"#summarization\">Summarization\u003C\u002Fa>\n    + \u003Ca href=\"#tagging-chunking-syntax-and-parsing\">Tagging, Chunking, Syntax and Parsing\u003C\u002Fa>\n    + \u003Ca href=\"#text-classification\">Text Classification\u003C\u002Fa>\n+ Machine Learning\n    + \u003Ca href=\"#architectures\">Architectures\u003C\u002Fa>\n    + \u003Ca href=\"#automl\">AutoML\u003C\u002Fa>\n    + \u003Ca href=\"#bayesian-methods\">Bayesian Methods\u003C\u002Fa>\n    + \u003Ca href=\"#classification-clustering-and-regression\">Classification, Clustering and Regression\u003C\u002Fa>\n    + \u003Ca href=\"#computer-vision\">Computer Vision\u003C\u002Fa>\n    + \u003Ca href=\"#contrastive-learning\">Contrastive Learning\u003C\u002Fa>\n    + \u003Ca href=\"#curriculum-learning\">Curriculum Learning\u003C\u002Fa>\n    + \u003Ca href=\"#data-augmentation\">Data Augmentation\u003C\u002Fa>\n    + \u003Ca href=\"#deep-learning-general-methods\">Deep Learning General Methods\u003C\u002Fa>\n    + \u003Ca href=\"#deep-reinforcement-learning\">Deep Reinforcement Learning\u003C\u002Fa>\n    + \u003Ca href=\"#diffusion-models\">Diffusion Models\u003C\u002Fa>\n    + \u003Ca href=\"#federated-learning\">Federated Learning\u003C\u002Fa>\n    + \u003Ca href=\"#few-shot-and-zero-shot-learning\">Few-Shot and Zero-Shot Learning\u003C\u002Fa>\n    + \u003Ca href=\"#general-machine-learning\">General Machine Learning\u003C\u002Fa>\n    + \u003Ca href=\"#generative-adversarial-networks\">Generative Adversarial Networks\u003C\u002Fa>\n    + \u003Ca href=\"#graph-neural-networks\">Graph Neural Networks\u003C\u002Fa>\n    + \u003Ca href=\"#interpretability-and-analysis\">Interpretability and Analysis\u003C\u002Fa>\n    + \u003Ca href=\"#knowledge-distillation\">Knowledge Distillation\u003C\u002Fa>\n    + \u003Ca href=\"#meta-learning\">Meta Learning\u003C\u002Fa>\n    + \u003Ca href=\"#metric-learning\">Metric Learning\u003C\u002Fa>\n    + \u003Ca href=\"#ml-and-dl-applications\">ML and DL Applications\u003C\u002Fa>\n    + \u003Ca href=\"#model-compression-and-acceleration\">Model Compression and Acceleration\u003C\u002Fa>\n    + \u003Ca href=\"#multi-label-learning\">Multi-Label Learning\u003C\u002Fa>\n    + \u003Ca href=\"#multi-task-and-multi-view-learning\">Multi-Task and Multi-View Learning\u003C\u002Fa>\n    + \u003Ca href=\"#online-learning\">Online Learning\u003C\u002Fa>\n    + \u003Ca href=\"#optimization\">Optimization\u003C\u002Fa>\n    + \u003Ca href=\"#semi-supervised-weakly-supervised-and-unsupervised-learning\">Semi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u003C\u002Fa>\n    + \u003Ca href=\"#transfer-learning\">Transfer Learning\u003C\u002Fa>\n    + \u003Ca href=\"#trustworthy-machine-learning\">Trustworthy Machine Learning\u003C\u002Fa>\n\n\nTo reduce class imbalance, we separate some of the hot sub-topics from the original categorization of ACL and ICML submissions. E.g., Named Entity Recognition is a first-level area in our categorization because it is the focus of several surveys.\n\n## Statistics\n\nWe show the number of paper in each area in Figures 1-2.\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_11989a8f8e54.png\" width=\"70%\" height=\"70%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">Figure 1: # of papers in each NLP area.\u003C\u002Fp>\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_8b289edf853b.png\" width=\"70%\" height=\"70%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">Figure 2:  # of papers in each ML area.\u003C\u002Fp>\n\nAlso, we plot paper number as a function of publication year (see Figure 3).\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_6a09c708d052.png\" width=\"70%\" height=\"70%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">Figure 3: # of papers vs publication year.\u003C\u002Fp>\n\nIn addition, we generate word clouds to show hot topics in these surveys (see Figures 4-5).\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_a57e935ae60d.png\" width=\"60%\" height=\"60%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">Figure 4: The word cloud for NLP.\u003C\u002Fp>\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_d498dc88fb3a.png\" width=\"60%\" height=\"60%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">Figure 5: The word cloud for ML.\u003C\u002Fp>\n\n\n## The NLP Paper List\n\n#### [Computational Social Science and Social Media](#content)\n\n1. **A Comprehensive Survey on Community Detection with Deep Learning.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.12584.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FSu2021A.md)\n\n    *Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cécile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu*\n\n2. **A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities.** ACM Comput. Surv. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.00315) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FZhou2021A.md)\n\n    *Xinyi Zhou, Reza Zafarani*\n\n3. **A Survey of Race, Racism, and Anti-Racism in NLP.** ACL 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.11410) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FField2021A.md)\n\n    *Anjalie Field, Su Lin Blodgett, Zeerak Waseem, Yulia Tsvetkov*\n\n4. **A Survey on Computational Propaganda Detection.** IJCAI 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.08024.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FMartino2020A.md)\n\n    *Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, Preslav Nakov*\n\n5. **A Survey on Trust Prediction in Online Social Networks.** IEEE Access 2020 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9142365) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FGhafari2020A.md)\n\n    *Seyed Mohssen Ghafari, Amin Beheshti, Aditya Joshi, Cécile Paris, Adnan Mahmood, Shahpar Yakhchi, Mehmet A. Orgun*\n\n6. **Computational Sociolinguistics: A Survey.** Comput. Linguistics 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1508.07544) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FNguyen2016Computational.md)\n\n    *Dong Nguyen, A. Seza Dogruöz, Carolyn P. Rosé, Franciska de Jong*\n\n7. **Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective.** J. Artif. Intell. Res. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.12305) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FKiritchenko2021Confronting.md)\n\n    *Svetlana Kiritchenko, Isar Nejadgholi, Kathleen C. Fraser*\n\n8. **From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science.** J. Soc. Comput. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.14198) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FChen2021From.md)\n\n    *Huimin Chen, Cheng Yang, Xuanming Zhang, Zhiyuan Liu, Maosong Sun, Jianbin Jin*\n\n9. **Language (Technology) is Power: A Critical Survey of \"Bias\" in NLP.** ACL 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.14050) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FBlodgett2020Language.md)\n\n    *Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach*\n\n10. **Societal Biases in Language Generation: Progress and Challenges.** ACL 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.04054.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FSheng2021Societal.md)\n\n    *Emily Sheng, Kai-Wei Chang, Prem Natarajan, Nanyun Peng*\n\n11. **Tackling Online Abuse: A Survey of Automated Abuse Detection Methods.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.06024.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FMishra2019Tackling.md)\n\n    *Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova*\n\n12. **When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?.** ACL 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.12043) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FJoseph2020When.md)\n\n    *Kenneth Joseph, Jonathan H. Morgan*\n\n#### [Dialogue and Interactive Systems](#content)\n\n1. **A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message.** arXiv 2015 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1505.03084) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FElmadany2015A.md)\n\n    *AbdelRahim A. Elmadany, Sherif M. Abdou, Mervat Gheith*\n\n2. **A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version.** Dialogue Discourse 2018 [paper](https:\u002F\u002Fjournals.uic.edu\u002Fojs\u002Findex.php\u002Fdad\u002Farticle\u002Fview\u002F10733\u002F9501) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FSerban2018A.md)\n\n    *Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau*\n\n3. **A Survey of Document Grounded Dialogue Systems (DGDS).** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.13818) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FMa2020A.md)\n\n    *Longxuan Ma, Wei-Nan Zhang, Mingda Li, Ting Liu*\n\n4. **A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.13211.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FLarson2022A.md)\n\n    *Stefan Larson, Kevin Leach*\n\n5. **A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.00500) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FSanthanam2019A.md)\n\n    *Sashank Santhanam, Samira Shaikh*\n\n6. **A survey of neural models for the automatic analysis of conversation: Towards a better integration of the social sciences.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.16891.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FClavel2022A.md)\n\n    *Chloé Clavel, Matthieu Labeau, Justine Cassell*\n\n7. **A Survey on Dialog Management: Recent Advances and Challenges.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.02233) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FDai2020A.md)\n\n    *Yinpei Dai, Huihua Yu, Yixuan Jiang, Chengguang Tang, Yongbin Li, Jian Sun*\n\n8. **A Survey on Dialogue Systems: Recent Advances and New Frontiers.** SIGKDD Explor. 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.01731) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FChen2017A.md)\n\n    *Hongshen Chen, Xiaorui Liu, Dawei Yin, Jiliang Tang*\n\n9. **Advances in Multi-turn Dialogue Comprehension: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03125) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FZhang2021Advances.md)\n\n    *Zhuosheng Zhang, Hai Zhao*\n\n10. **Challenges in Building Intelligent Open-domain Dialog Systems.** ACM Trans. Inf. Syst. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.05709) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FHuang2020Challenges.md)\n\n    *Minlie Huang, Xiaoyan Zhu, Jianfeng Gao*\n\n11. **Conversational Agents: Theory and Applications.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.03164.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FWahde2022Conversational.md)\n\n    *Mattias Wahde, Marco Virgolin*\n\n12. **Conversational Machine Comprehension: a Literature Review.** COLING 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.00671) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FGupta2020Conversational.md)\n\n    *Somil Gupta, Bhanu Pratap Singh Rawat, Hong Yu*\n\n13. **How to Evaluate Your Dialogue Models: A Review of Approaches.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.01369.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FLi2021How.md)\n\n    *Xinmeng Li, Wansen Wu, Long Qin, Quanjun Yin*\n\n14. **Neural Approaches to Conversational AI.** ACL 2018 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.08267) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FGao2018Neural.md)\n\n    *Jianfeng Gao, Michel Galley, Lihong Li*\n\n15. **Neural Approaches to Conversational AI: Question Answering, Task-oriented Dialogues and Social Chatbots.** Now Foundations and Trends 2019 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8649787) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FGao2019Neural.md)\n\n    *Jianfeng Gao, Michel Galley, Lihong Li*\n\n16. **POMDP-Based Statistical Spoken Dialog Systems: A Review.** Proc. IEEE 2013 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6407655\u002F) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FYoung2013POMDP-Based.md)\n\n    *Steve J. Young, Milica Gasic, Blaise Thomson, Jason D. Williams*\n\n17. **Recent Advances and Challenges in Task-oriented Dialog System.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.07490) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FZhang2020Recent.md)\n\n    *Zheng Zhang, Ryuichi Takanobu, Minlie Huang, Xiaoyan Zhu*\n\n18. **Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.04387.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FNi2021Recent.md)\n\n    *Jinjie Ni, Tom Young, Vlad Pandelea, Fuzhao Xue, Vinay Adiga, Erik Cambria*\n\n19. **Utterance-level Dialogue Understanding: An Empirical Study.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.13902) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FGhosal2020Utterance-level.md)\n\n    *Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria*\n\n#### [Generation](#content)\n\n1. **A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.05337.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FZhang2022A.md)\n\n    *Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song*\n\n2. **A Survey of Knowledge-Enhanced Text Generation.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.04389.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FYu2022A.md)\n\n    *Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang*\n\n3. **A Survey on Multi-hop Question Answering and Generation.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.09140.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FMavi2022A.md)\n\n    *Vaibhav Mavi, Anubhav Jangra, Adam Jatowt*\n\n4. **A Survey on Retrieval-Augmented Text Generation.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.01110.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FLi2022A.md)\n\n    *Huayang Li, Yixuan Su, Deng Cai, Yan Wang, Lemao Liu*\n\n5. **A Survey on Text Simplification.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.08612) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FSikka2020A.md)\n\n    *Punardeep Sikka, Vijay Mago*\n\n6. **Automatic Detection of Machine Generated Text: A Critical Survey.** COLING 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.01314.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FJawahar2020Automatic.md)\n\n    *Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan*\n\n7. **Automatic Story Generation: Challenges and Attempts.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.12634) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FAlabdulkarim2021Automatic.md)\n\n    *Amal Alabdulkarim, Siyan Li, Xiangyu Peng*\n\n8. **ChatGPT is not all you need. A State of the Art Review of large Generative AI models.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.04655.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FGozalo-Brizuela2023ChatGPT.md)\n\n    *Roberto Gozalo-Brizuela, Eduardo C. Garrido-Merchán*\n\n9. **Content Selection in Data-to-Text Systems: A Survey.** arXiv 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.08375) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FGkatzia2016Content.md)\n\n    *Dimitra Gkatzia*\n\n10. **Data-Driven Sentence Simplification: Survey and Benchmark.** Comput. Linguistics 2020 [paper](https:\u002F\u002Fwww.mitpressjournals.org\u002Fdoi\u002Fpdf\u002F10.1162\u002FCOLI_a_00370) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FAlva-Manchego2020Data-Driven.md)\n\n    *Fernando Alva-Manchego, Carolina Scarton, Lucia Specia*\n\n11. **Deep Learning for Text Style Transfer: A Survey.** Comput. Linguistics 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.00416.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FJin2022Deep.md)\n\n    *Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea*\n\n12. **Evaluation of Text Generation: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.14799) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FCelikyilmaz2020Evaluation.md)\n\n    *Asli Celikyilmaz, Elizabeth Clark, Jianfeng Gao*\n\n13. **Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.00308.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FHämäläinen2021Human.md)\n\n    *Mika Hämäläinen, Khalid Al-Najjar*\n\n14. **Keyphrase Generation: A Multi-Aspect Survey.** FRUCT 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.05059) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FÇano2019Keyphrase.md)\n\n    *Erion Çano, Ondrej Bojar*\n\n15. **Neural Language Generation: Formulation, Methods, and Evaluation.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.15780.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FGarbacea2020Neural.md)\n\n    *Cristina Garbacea, Qiaozhu Mei*\n\n16. **Neural Text Generation: Past, Present and Beyond.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.07133.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FLu2018Neural.md)\n\n    *Sidi Lu, Yaoming Zhu, Weinan Zhang, Jun Wang, Yong Yu*\n\n17. **Quiz-Style Question Generation for News Stories.** WWW 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.09094) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FLelkes2021Quiz-Style.md)\n\n    *Ádám D. Lelkes, Vinh Q. Tran, Cong Yu*\n\n18. **Recent Advances in Neural Question Generation.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.08949) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FPan2019Recent.md)\n\n    *Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yen Kan*\n\n19. **Recent Advances in SQL Query Generation: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07667) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FKalajdjieski2020Recent.md)\n\n    *Jovan Kalajdjieski, Martina Toshevska, Frosina Stojanovska*\n\n20. **Survey of Hallucination in Natural Language Generation.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.03629.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FJi2022Survey.md)\n\n    *Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, Pascale Fung*\n\n21. **Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation.** J. Artif. Intell. Res. 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.09902) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FGatt2018Survey.md)\n\n    *Albert Gatt, Emiel Krahmer*\n\n#### [Information Extraction](#content)\n\n1. **A Review on Fact Extraction and Verification.** ACM Comput. Surv. 2023 [paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F2010.03001) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FBekoulis2023A.md)\n\n    *Giannis Bekoulis, Christina Papagiannopoulou, Nikos Deligiannis*\n\n2. **A Survey of Deep Learning Methods for Relation Extraction.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.03645) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FKumar2017A.md)\n\n    *Shantanu Kumar*\n\n3. **A Survey of Event Extraction From Text.** IEEE Access 2019 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8918013) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FXiang2019A.md)\n\n    *Wei Xiang, Bang Wang*\n\n4. **A Survey of event extraction methods from text for decision support systems.** Decis. Support Syst. 2016 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0167923616300173) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FHogenboom2016A.md)\n\n    *Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong, Emiel Caron*\n\n5. **A survey of joint intent detection and slot-filling models in natural language understanding.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08091) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FWeld2021A.md)\n\n    *Henry Weld, Xiaoqi Huang, Siqi Long, Josiah Poon, Soyeon Caren Han*\n\n6. **A Survey of Textual Event Extraction from Social Networks.** LPKM 2017 [paper](http:\u002F\u002Fceur-ws.org\u002FVol-1988\u002FLPKM2017_paper_15.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FMejri2017A.md)\n\n    *Mohamed Mejri, Jalel Akaichi*\n\n7. **A Survey on Deep Learning Event Extraction: Approaches and Applications.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.02126.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FLi2021A.md)\n\n    *Qian Li, Jianxin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. 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Pan, Huajun Chen*\n\n15. **More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction.** AACL 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.03186) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FHan2020More.md)\n\n    *Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun*\n\n16. **Neural relation extraction: a survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.04247) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FAydar2020Neural.md)\n\n    *Mehmet Aydar, Ozge Bozal, Furkan Özbay*\n\n17. **No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.01712.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FSilva2022No.md)\n\n    *Marília Costa Rosendo Silva, Felipe Alves Siqueira, João Pedro Mantovani Tarrega, João Vitor Pataca Beinotti, Augusto Sousa Nunes, Miguel de Mattos Gardini, Vinícius Adolfo Pereira da Silva, Nádia Félix Felipe da Silva, André Carlos Ponce de Leon Ferreira de Carvalho*\n\n18. **Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey.** COLING 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.00564) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FLouvan2020Recent.md)\n\n    *Samuel Louvan, Bernardo Magnini*\n\n19. **Relation Extraction : A Survey.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.05191) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FPawar2017Relation.md)\n\n    *Sachin Pawar, Girish K. Palshikar, Pushpak Bhattacharyya*\n\n20. **Techniques for Jointly Extracting Entities and Relations: A Survey.** CICLing 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06118) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FPawar2019Techniques.md)\n\n    *Sachin Pawar, Pushpak Bhattacharyya, Girish K. Palshikar*\n\n#### [Information Retrieval and Text Mining](#content)\n\n1. **A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.02919) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FAllahyari2017A.md)\n\n    *Mehdi Allahyari, Seyed Amin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys J. Kochut*\n\n2. **A survey of methods to ease the development of highly multilingual text mining applications.** Lang. Resour. Evaluation 2012 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1401.2937) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FSteinberger2012A.md)\n\n    *Ralf Steinberger*\n\n3. **A Survey on Retrieval-Augmented Text Generation.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.01110.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FLi2022A.md)\n\n    *Huayang Li, Yixuan Su, Deng Cai, Yan Wang, Lemao Liu*\n\n4. **Data Mining and Information Retrieval in the 21st century: A bibliographic review.** Comput. Sci. Rev. 2019 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1574013719301297) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FLiu2019Data.md)\n\n    *Jiaying Liu, Xiangjie Kong, Xinyu Zhou, Lei Wang, Da Zhang, Ivan Lee, Bo Xu, Feng Xia*\n\n5. **Dense Text Retrieval based on Pretrained Language Models: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.14876.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FZhao2022Dense.md)\n\n    *Wayne Xin Zhao, Jing Liu, Ruiyang Ren, Ji-Rong Wen*\n\n6. **Neural Entity Linking: A Survey of Models Based on Deep Learning.** Semantic Web 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.00575) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FSevgili2022Neural.md)\n\n    *Özge Sevgili, Artem Shelmanov, Mikhail Y. Arkhipov, Alexander Panchenko, Chris Biemann*\n\n7. **Neural Models for Information Retrieval.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.01509.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FMitra2017Neural.md)\n\n    *Bhaskar Mitra, Nick Craswell*\n\n8. **Opinion Mining and Analysis: A survey.** arXiv 2013 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1307.3336) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FBuche2013Opinion.md)\n\n    *Arti Buche, M. B. Chandak, Akshay Zadgaonkar*\n\n9. **Pre-training Methods in Information Retrieval.** Found. Trends Inf. 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Data Eng. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.07695) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FQiang2022Short.md)\n\n    *Jipeng Qiang, Zhenyu Qian, Yun Li, Yunhao Yuan, Xindong Wu*\n\n12. **Taking Search to Task.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.05046.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FShah2023Taking.md)\n\n    *Chirag Shah, Ryen W. White, Paul Thomas, Bhaskar Mitra, Shawon Sarkar, Nicholas J. Belkin*\n\n13. **Topic Modelling Meets Deep Neural Networks: A Survey.** IJCAI 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00498) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FZhao2021Topic.md)\n\n    *He Zhao, Dinh Q. Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. 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A Survey Organizing Contextualized Encoders.** EMNLP 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.00854.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInterpretability-and-Analysis-of-Models-for-NLP\u002FXia2020Which.md)\n\n    *Patrick Xia, Shijie Wu, Benjamin Van Durme*\n\n#### [Knowledge Graph](#content)\n\n1. **A Review of Relational Machine Learning for Knowledge Graphs.** Proc. 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Res. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.09358) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FLanguage-Grounding-to-Vision,-Robotics-and-Beyond\u002FMogadala2021Trends.md)\n\n    *Aditya Mogadala, Marimuthu Kalimuthu, Dietrich Klakow*\n\n#### [Large Language Models](#content)\n\n1. **A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.04226) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FLarge-Language-Models\u002FCao2023A.md)\n\n    *Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S. Yu, Lichao Sun*\n\n2. **A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.09419) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FLarge-Language-Models\u002FZhou2023A.md)\n\n    *Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, Jianxin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun*\n\n3. **A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.11391) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FLarge-Language-Models\u002FHuang2023A.md)\n\n    *Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin, Yi Dong, Changshun Wu, Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, Andre Freitas, Mustafa A. Mustafa*\n\n4. **A Survey on In-context Learning.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.00234) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FLarge-Language-Models\u002FDong2023A.md)\n\n    *Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, Zhifang Sui*\n\n5. **A Survey of Large Language Models.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.18223) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FLarge-Language-Models\u002FZhao2023A.md)\n\n    *Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen*\n\n6. **AI-Augmented Surveys: Leveraging Large Language Models for Opinion Prediction in Nationally Representative Surveys.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.09620) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FLarge-Language-Models\u002FKim2023AI-Augmented.md)\n\n    *Junsol Kim, Byungkyu Lee*\n\n7. **Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.00955) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FLarge-Language-Models\u002FFernandes2023Bridging.md)\n\n    *Patrick Fernandes, Aman Madaan, Emmy Liu, António Farinhas, Pedro Henrique Martins, Amanda Bertsch, José G. 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Milder, Colin Raffel, Edwin Simpson, Noam Slonim, Niranjan Balasubramanian, Leon Derczynski, Roy Schwartz*\n\n23. **Experience Grounds Language.** EMNLP 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.10151) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FBisk2020Experience.md)\n\n    *Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph P. Turian*\n\n24. **How Commonsense Knowledge Helps with Natural Language Tasks: A Survey of Recent Resources and Methodologies.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.04674.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FXie2021How.md)\n\n    *Yubo Xie, Pearl Pu*\n\n25. **Jumping NLP curves: A review of natural language processing research [Review Article].** IEEE Comput. Intell. Mag. 2014 [paper](http:\u002F\u002Fkrchowdhary.com\u002Fai\u002Fai14\u002Flects\u002Fnlp-research-com-intlg-ieee.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FCambria2014Jumping.md)\n\n    *Erik Cambria, Bebo White*\n\n26. **Meta Learning for Natural Language Processing: A Survey.** NAACL-HLT 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.01500.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FLee2022Meta.md)\n\n    *Hung-yi Lee, Shang-Wen Li, Thang Vu*\n\n27. **Natural Language Processing - A Survey.** arXiv 2012 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1209.6238) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FMote2012Natural.md)\n\n    *Kevin Mote*\n\n28. **Natural Language Processing: State of The Art, Current Trends and Challenges.** Multim. Tools Appl. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.05148) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FKhurana2023Natural.md)\n\n    *Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh*\n\n29. **Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering.** COLING 2018 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.04330.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FLan2018Neural.md)\n\n    *Wuwei Lan, Wei Xu*\n\n30. **Overview of the Transformer-based Models for NLP Tasks.** FedCSIS 2020 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9222960) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FGillioz2020Overview.md)\n\n    *Anthony Gillioz, Jacky Casas, Elena Mugellini, Omar Abou Khaled*\n\n31. **Paradigm Shift in Natural Language Processing.** Int. J. Autom. Comput. 2022 [paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11633-022-1331-6) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FSun2022Paradigm.md)\n\n    *Tianxiang Sun, Xiangyang Liu, Xipeng Qiu, Xuan-Jing Huang*\n\n32. **Progress in Neural NLP: Modeling, Learning, and Reasoning.** Engineering 2020 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2095809919304928) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FZhou2020Progress.md)\n\n    *Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum*\n\n33. **Putting Humans in the Natural Language Processing Loop: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.04044) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FWang2021Putting.md)\n\n    *Zijie J. Wang, Dongjin Choi, Shenyu Xu, Diyi Yang*\n\n34. **State-of-the-art generalisation research in NLP: A taxonomy and review.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.03050.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FHupkes2022State-of-the-art.md)\n\n    *Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, Christos Christodoulopoulos, Karim Lasri, Naomi Saphra, Arabella Sinclair, Dennis Ulmer, Florian Schottmann, Khuyagbaatar Batsuren, Kaiser Sun, Koustuv Sinha, Leila Khalatbari, Maria Ryskina, Rita Frieske, Ryan Cotterell, Zhijing Jin*\n\n35. **Survey on Publicly Available Sinhala Natural Language Processing Tools and Research.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.02358) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FSilva2019Survey.md)\n\n    *Nisansa de Silva*\n\n36. **Visualizing Natural Language Descriptions: A Survey.** ACM Comput. Surv. 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1607.00623) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FHassani2016Visualizing.md)\n\n    *Kaveh Hassani, Won-Sook Lee*\n\n37. **Word Alignment in the Era of Deep Learning: A Tutorial.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.00138.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FLi2022Word.md)\n\n    *Bryan Li*\n\n#### [NLP Applications](#content)\n\n1. **A Short Survey of Biomedical Relation Extraction Techniques.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05850) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FShahab2017A.md)\n\n    *Elham Shahab*\n\n2. **A Survey of Learning-based Automated Program Repair.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.03270.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FZhang2023A.md)\n\n    *Quanjun Zhang, Chunrong Fang, Yuxiang Ma, Weisong Sun, Zhenyu Chen*\n\n3. **A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.04859.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FCui2022A.md)\n\n    *Junyun Cui, Xiaoyu Shen, Feiping Nie, Zheng Wang, Jinglong Wang, Yulong Chen*\n\n4. **A survey on natural language processing (nlp) and applications in insurance.** arXiv 2020 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.00462.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FLy2020A.md)\n\n    *Antoine Ly, Benno Uthayasooriyar, Tingting Wang*\n\n5. **Android Security using NLP Techniques: A Review.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.03072 ) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FSen2021Android.md)\n\n    *Sevil Sen, Burcu Can*\n\n6. **Disinformation Detection: A review of linguistic feature selection and classification models in news veracity assessments.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.12073) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FTompkins2019Disinformation.md)\n\n    *Jillian Tompkins*\n\n7. **Extraction and Analysis of Fictional Character Networks: A Survey.** ACM Comput. Surv. 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.02704) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FLabatut2019Extraction.md)\n\n    *Vincent Labatut, Xavier Bost*\n\n8. **How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence.** ACL 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2004.12158) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FZhong2020How.md)\n\n    *Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, Maosong Sun*\n\n9. **Natural Language Based Financial Forecasting: A Survey.** Artif. Intell. Rev. 2018 [paper](https:\u002F\u002Fdspace.mit.edu\u002Fbitstream\u002Fhandle\u002F1721.1\u002F116314\u002F10462_2017_9588_ReferencePDF.pdf?sequence=2&isAllowed=y) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FXing2018Natural.md)\n\n    *Frank Z. Xing, Erik Cambria, Roy E. Welsch*\n\n10. **Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review.** arXiv 2021 [paper]( https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.02975 ) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FLi2021Neural.md)\n\n    *Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlali, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, Richard Andrew Taylor, Harlan M. Krumholz, Dragomir R. Radev*\n\n11. **SECNLP: A survey of embeddings in clinical natural language processing.** J. Biomed. Informatics 2020 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1532046419302436) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FKalyan2020SECNLP.md)\n\n    *Katikapalli Subramanyam Kalyan, Sivanesan Sangeetha*\n\n12. **Survey of Natural Language Processing Techniques in Bioinformatics.** Comput. Math. Methods Medicine 2015 [paper](https:\u002F\u002Fpdfs.semanticscholar.org\u002F7013\u002F479be7dda124750aa22fb6231eea2671f630.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FZeng2015Survey.md)\n\n    *Zhiqiang Zeng, Hua Shi, Yun Wu, Zhiling Hong*\n\n13. **Survey of Text-based Epidemic Intelligence: A Computational Linguistics Perspective.** ACM Comput. Surv. 2020 [paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3361141) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FJoshi2020Survey.md)\n\n    *Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cécile Paris, C. Raina MacIntyre*\n\n14. **The Potential of Machine Learning and NLP for Handling Students' Feedback (A Short Survey).** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.05806) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FEdalati2020The.md)\n\n    *Maryam Edalati*\n\n15. **Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.14445.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FNLP-Applications\u002FMa2020Towards.md)\n\n    *Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi*\n\n#### [Pretrained Models](#content)\n\n1. **A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.12982) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FRethmeier2021A.md)\n\n    *Nils Rethmeier, Isabelle Augenstein*\n\n2. **A Review on Language Models as Knowledge Bases.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.06031.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FAlKhamissi2022A.md)\n\n    *Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, Mona T. Diab, Marjan Ghazvininejad*\n\n3. **A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.10810) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FZaib2021A.md)\n\n    *Munazza Zaib, Quan Z. Sheng, Wei Emma Zhang*\n\n4. **A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.05337.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FZhang2022A.md)\n\n    *Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song*\n\n5. **A Survey of Vision-Language Pre-Trained Models.** IJCAI 2022 [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0762.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FDu2022A.md)\n\n    *Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao*\n\n6. **A Survey on Time-Series Pre-Trained Models.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.10716) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FMa2023A.md)\n\n    *Qianli Ma, Zhen Liu, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, James T. Kwok*\n\n7. **AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.05542.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FKalyan2021AMMUS.md)\n\n    *Katikapalli Subramanyam Kalyan, Ajit Rajasekharan, Sivanesan Sangeetha*\n\n8. **Commonsense Reasoning for Conversational AI: A Survey of the State of the Art.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.07926) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FRichardson2023Commonsense.md)\n\n    *Christopher Richardson, Larry Heck*\n\n9. **Dense Text Retrieval based on Pretrained Language Models: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.14876.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FZhao2022Dense.md)\n\n    *Wayne Xin Zhao, Jing Liu, Ruiyang Ren, Ji-Rong Wen*\n\n10. **Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing.** ACM Comput. Surv. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.13586.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FLiu2023Pre-train.md)\n\n    *Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig*\n\n11. **Pretrained Language Models for Text Generation: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.05273) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FLi2021Pretrained.md)\n\n    *Junyi Li, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen*\n\n12. **Pre-trained models for natural language processing: A survey.** arXiv 2020 [paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11431-020-1647-3.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FQiu2020Pre-trained.md)\n\n    *Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, Xuanjing Huang*\n\n13. **Pre-Trained Models: Past, Present and Future.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.07139) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FHan2021Pre-Trained.md)\n\n    *Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, Jun Zhu*\n\n14. **Pretrained Transformers for Text Ranking: BERT and Beyond.** WSDM 2021 [paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3437963.3441667) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FYates2021Pretrained.md)\n\n    *Andrew Yates, Rodrigo Nogueira, Jimmy Lin*\n\n15. **Pre-training Methods in Information Retrieval.** Found. Trends Inf. Retr. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.13853.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FFan2022Pre-training.md)\n\n    *Yixing Fan, Xiaohui Xie, Yinqiong Cai, Jia Chen, Xinyu Ma, Xiangsheng Li, Ruqing Zhang, Jiafeng Guo*\n\n16. **Survey: Transformer based Video-Language Pre-training.** AI Open 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.09920.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FRuan2022Survey.md)\n\n    *Ludan Ruan, Qin Jin*\n\n17. **VLP: A Survey on Vision-Language Pre-training.** Int. J. Autom. Comput. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.09061.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPretrained-Models\u002FChen2023VLP.md)\n\n    *Feilong Chen, Duzhen Zhang, Minglun Han, Xiu-Yi Chen, Jing Shi, Shuang Xu, Bo Xu*\n\n#### [Prompt](#content)\n\n1. **Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.10475) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPrompt\u002FLou2023Is.md)\n\n    *Renze Lou, Kai Zhang, Wenpeng Yin*\n\n2. **OpenPrompt: An Open-source Framework for Prompt-learning.** ACL 2022 [paper](https:\u002F\u002Faclanthology.org\u002F2022.acl-demo.10.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPrompt\u002FDing2022OpenPrompt.md)\n\n    *Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Haitao Zheng, Maosong Sun*\n\n3. **Reasoning with Language Model Prompting: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.09597.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FPrompt\u002FQiao2022Reasoning.md)\n\n    *Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen*\n\n#### [Question Answering](#content)\n\n1. **A Survey of Question Answering over Knowledge Base.** CCKS 2019 [paper](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-981-15-1956-7_8) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FQuestion-Answering\u002FWu2019A.md)\n\n    *Peiyun Wu, Xiaowang Zhang, Zhiyong Feng*\n\n2. **A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions.** IJCAI 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.11644) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FQuestion-Answering\u002FLan2021A.md)\n\n    *Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen*\n\n3. **A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.13069) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FQuestion-Answering\u002FFu2020A.md)\n\n    *Bin Fu, Yunqi Qiu, Chengguang Tang, Yang Li, Haiyang Yu, Jian Sun*\n\n4. **A Survey on Multi-hop Question Answering and Generation.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.09140.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FQuestion-Answering\u002FMavi2022A.md)\n\n    *Vaibhav Mavi, Anubhav Jangra, Adam Jatowt*\n\n5. **A survey on question answering technology from an information retrieval perspective.** Inf. Sci. 2011 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025511003860) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FQuestion-Answering\u002FKolomiyets2011A.md)\n\n    *Oleksandr Kolomiyets, Marie-Francine Moens*\n\n6. **A Survey on Why-Type Question Answering Systems.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.04879) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FQuestion-Answering\u002FBreja2019A.md)\n\n    *Manvi Breja, Sanjay Kumar Jain*\n\n7. **Complex Knowledge Base Question Answering: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.06688.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FQuestion-Answering\u002FLan2021Complex.md)\n\n    *Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen*\n\n8. **Core techniques of question answering systems over knowledge bases: a survey.** Knowl. Inf. Syst. 2018 [paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10115-017-1100-y) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FQuestion-Answering\u002FDiefenbach2018Core.md)\n\n    *Dennis Diefenbach, Vanessa López, Kamal Deep Singh, Pierre Maret*\n\n9. **Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.09361) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FQuestion-Answering\u002FChakraborty2019Introduction.md)\n\n    *Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer*\n\n10. **Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study.** Trans. Assoc. Comput. 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Khapra*\n\n5. **A Survey of Word Embeddings Evaluation Methods.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.09536) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FBakarov2018A.md)\n\n    *Amir Bakarov*\n\n6. **A Survey on Recognizing Textual Entailment as an NLP Evaluation.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.03061.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FPoliak2020A.md)\n\n    *Adam Poliak*\n\n7. **Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources.** EMNLP 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.15649.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FYu2022Beyond.md)\n\n    *Xinyan Yu, Trina Chatterjee, Akari Asai, Junjie Hu, Eunsol Choi*\n\n8. **Corpora Annotated with Negation: An Overview.** Comput. Linguistics 2020 [paper](https:\u002F\u002Fwatermark.silverchair.com\u002Fcoli_a_00371.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAqUwggKhBgkqhkiG9w0BBwagggKSMIICjgIBADCCAocGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMwFfpYsXe-j1WZLOYAgEQgIICWK8_os-_3bOw2Egxl-QP8k6_eaUBXbfLcdwSiN1AKd2RyuDFyjIlDYSZ5NTAAsDgDlMCD3TrhPG0ikKF7P7kuegNT5PvSubob_GmEmkrscxcBW6EJJepel-bEup-_A22uwRLCznueNRO_TIF1YCNc5jsTEopV_PzSEeI-vqG3BTbc_EtWxty9udu1sZYsHmXO2i8h7_m5MGt3nCX8aXXNkRPhrmNZ4IHU2moi76_JOuBQb6U6n6SItsdwObWewSPB3eGmx4DmUboNcB-Dv7OJAS9jmWHgsNzsSiRw9lRBcsf1O_0Nkv5YkFSkVNTiCldQ3B1fWgjDN0GWSOTsMS-6Je6keFnovcc8nQnxw-ubXQ57UZYQjZHa8jg6Ea1kOUHJem8uRdc4IMJuKCunIKRJLT1SSLFGYDgehwxQfOQk-H6LOIsbWOaiXwP9aDDqG4a6Pxl_bwnpi8JUp5dQYvqLNteQ-rjGS8FbRvlaV34wL49UAEBwa2DFlkTVhebzCkrzuzN-H3obLkhqnR-LDXbjSQhYOzROGh74Gq-beWVM7boVegN49iq-El7CzRqnoTIzVjtBrp3b-tnaevilOo05l0s2rhFLr-46GRyXgD11UTbz0tCy892aJACw6XYCsRvx2veM2tzBxg5D6a65ev1F3ViYbOlyz99M11QLllIMdoRT1R5fkdEyFrDQh-Q6VCJT3tJAOdlhWCc6kpie4jME3xACsVXSKXIW4q7OCXDHtdvmQnUWWJURJAYZ2Rwfvc9JwQ20jY37wr5ZyyQ8VuiRXwkiiOK4EScHg) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FZafra2020Corpora.md)\n\n    *Salud María Jiménez Zafra, Roser Morante, María Teresa Martín-Valdivia, Luis Alfonso Ureña López*\n\n9. **Critical Survey of the Freely Available Arabic Corpora.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.07835) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FZaghouani2017Critical.md)\n\n    *Wajdi Zaghouani*\n\n10. **Efficient Methods for Natural Language Processing: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.00099.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FTreviso2022Efficient.md)\n\n    *Marcos V. 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Milder, Colin Raffel, Edwin Simpson, Noam Slonim, Niranjan Balasubramanian, Leon Derczynski, Roy Schwartz*\n\n11. **Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.01172) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FStorks2019Recent.md)\n\n    *Shane Storks, Qiaozi Gao, Joyce Y Chai*\n\n12. **Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06935.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FGehrmann2022Repairing.md)\n\n    *Sebastian Gehrmann, Elizabeth Clark, Thibault Sellam*\n\n13. **Survey on Evaluation Methods for Dialogue Systems.** Artif. Intell. Rev. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.04071) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FDeriu2021Survey.md)\n\n    *Jan Deriu, Álvaro Rodrigo, Arantxa Otegi, Guillermo Echegoyen, Sophie Rosset, Eneko Agirre, Mark Cieliebak*\n\n14. **Survey on Publicly Available Sinhala Natural Language Processing Tools and Research.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.02358) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FSilva2019Survey.md)\n\n    *Nisansa de Silva*\n\n15. **The Great Misalignment Problem in Human Evaluation of NLP Methods.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.05361) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FHämäläinen2021The.md)\n\n    *Mika Hämäläinen, Khalid Al-Najjar*\n\n16. **Towards Standard Criteria for human evaluation of Chatbots: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.11197) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FResources-and-Evaluation\u002FLiang2021Towards.md)\n\n    *Hongru Liang, Huaqing Li*\n\n#### [Semantics](#content)\n\n1. **A reproducible experimental survey on biomedical sentence similarity: a string-based method sets the state of the art.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.08740.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSemantics\u002FLara-Clares2022A.md)\n\n    *Alicia Lara-Clares, Juan J. 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Syst. 2019 [paper](http:\u002F\u002Fcse.iitkgp.ac.in\u002F~saptarshi\u002Fcourses\u002Fsocomp2020a\u002Fsentiment-analysis-survey-yue2019.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FYue2019A.md)\n\n    *Lin Yue, Weitong Chen, Xue Li, Wanli Zuo, Minghao Yin*\n\n4. **A Survey On Semantic Steganography Systems.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12425.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FFigueira2022A.md)\n\n    *João Figueira*\n\n5. **A Survey on Sentiment and Emotion Analysis for Computational Literary Studies.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.03137) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FKim2018A.md)\n\n    *Evgeny Kim, Roman Klinger*\n\n6. **Automatic Sarcasm Detection: A Survey.** ACM Comput. 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Hovy*\n\n11. **Fine-grained Financial Opinion Mining: A Survey and Research Agenda.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.01897.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FChen2020Fine-grained.md)\n\n    *Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen*\n\n12. **On Positivity Bias in Negative Reviews.** ACL 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.12056) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FAithal2021On.md)\n\n    *Madhusudhan Aithal, Chenhao Tan*\n\n13. **Sarcasm Detection: A Comparative Study.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.02276) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FYaghoobian2021Sarcasm.md)\n\n    *Hamed Yaghoobian, Hamid R. Arabnia, Khaled Rasheed*\n\n14. **Sentiment analysis algorithms and applications: A survey.** Ain Shams Engineering Journal 2014 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2090447914000550) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FMedhat2014Sentiment.md)\n\n    *Walaa Medhat, Ahmed Hassan, Hoda Korashy*\n\n15. **Sentiment analysis for Arabic language: A brief survey of approaches and techniques.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02782) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FAlrefai2018Sentiment.md)\n\n    *Mo'ath Alrefai, Hossam Faris, Ibrahim Aljarah*\n\n16. **Sentiment Analysis of Czech Texts: An Algorithmic Survey.** ICAART 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.02780) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FÇano2019Sentiment.md)\n\n    *Erion Çano, Ondrej Bojar*\n\n17. **Sentiment Analysis of Twitter Data: A Survey of Techniques.** IJCAI 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.06971) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FVishal.A.Kharde2016Sentiment.md)\n\n    *Vishal.A.Kharde, Prof. Sheetal.Sonawane*\n\n18. **Sentiment Analysis on YouTube: A Brief Survey.** arXiv 2015 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.09142) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FAsghar2015Sentiment.md)\n\n    *Muhammad Zubair Asghar, Shakeel Ahmad, Afsana Marwat, Fazal Masood Kundi*\n\n19. **Sentiment\u002FSubjectivity Analysis Survey for Languages other than English.** Soc. Netw. Anal. Min. 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.00087) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FKorayem2016Sentiment.md)\n\n    *Mohammed Korayem, Khalifeh AlJadda, David J. Crandall*\n\n20. **Survey of Aspect-based Sentiment Analysis Datasets.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.05232.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FChebolu2022Survey.md)\n\n    *Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio*\n\n21. **Towards Argument Mining for Social Good: A Survey.** ACL 2021 [paper](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.107.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FVecchi2021Towards.md)\n\n    *Eva Maria Vecchi, Neele Falk, Iman Jundi, Gabriella Lapesa*\n\n22. **Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.00753) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FÇano2019Word.md)\n\n    *Erion Çano, Maurizio Morisio*\n\n#### [Speech and Multimodality](#content)\n\n1. **A Comparative Analysis of Techniques and Algorithms for Recognising Sign Language.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13941) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FKumar2023A.md)\n\n    *Rupesh Kumar, Ayush Sinha, Ashutosh Bajpai, S. K Singh*\n\n2. **A Comprehensive Survey on Cross-modal Retrieval.** arXiv 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1607.06215) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FWang2016A.md)\n\n    *Kaiye Wang, Qiyue Yin, Wei Wang, Shu Wu, Liang Wang*\n\n3. **A Multimodal Memes Classification: A Survey and Open Research Issues.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08395) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FAfridi2020A.md)\n\n    *Tariq Habib Afridi, Aftab Alam, Muhammad Numan Khan, Jawad Khan, Young-Koo Lee*\n\n4. **A Survey : Neural Networks for AMR-to-Text.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.07328.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FHao2022A.md)\n\n    *Hongyu Hao, Guangtong Li, Zhiming Hu, Huafeng Wang*\n\n5. **A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis.** WIREs Data Mining Knowl. Discov. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.09399) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FAgnese2020A.md)\n\n    *Jorge Agnese, Jonathan Herrera, Haicheng Tao, Xingquan Zhu*\n\n6. **A Survey of Code-switched Speech and Language Processing.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.00784) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FSitaram2019A.md)\n\n    *Sunayana Sitaram, Khyathi Raghavi Chandu, Sai Krishna Rallabandi, Alan W. Black*\n\n7. **A Survey of Deep Learning Approaches for OCR and Document Understanding.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.13534.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FSubramani2020A.md)\n\n    *Nishant Subramani, Alexandre Matton, Malcolm Greaves, Adrian Lam*\n\n8. **A Survey of Recent DNN Architectures on the TIMIT Phone Recognition Task.** TSD 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07974) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FMichálek2018A.md)\n\n    *Josef Michálek, Jan Vanek*\n\n9. **A Survey of Vision-Language Pre-Trained Models.** IJCAI 2022 [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0762.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FDu2022A.md)\n\n    *Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao*\n\n10. **A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder.** arXiv 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.03934) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FKrupakar2016A.md)\n\n    *Hans Krupakar, Keerthika Rajvel, Bharathi B, Angel Deborah S, Vallidevi Krishnamurthy*\n\n11. **A Survey on Neural Speech Synthesis.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.15561.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FTan2021A.md)\n\n    *Xu Tan, Tao Qin, Frank K. Soong, Tie-Yan Liu*\n\n12. **A Survey on Spoken Language Understanding: Recent Advances and New Frontiers.** IJCAI 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03095) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FQin2021A.md)\n\n    *Libo Qin, Tianbao Xie, Wanxiang Che, Ting Liu*\n\n13. **A Thorough Review on Recent Deep Learning Methodologies for Image Captioning.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.13114.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FElhagry2021A.md)\n\n    *Ahmed Elhagry, Karima Kadaoui*\n\n14. **Accented Speech Recognition: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.10747) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FHinsvark2021Accented.md)\n\n    *Arthur Hinsvark, Natalie Delworth, Miguel Del Rio, Quinten McNamara, Joshua Dong, Ryan Westerman, Michelle Huang, Joseph Palakapilly, Jennifer Drexler, Ilya Pirkin, Nishchal Bhandari, Miguel Jette*\n\n15. **Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures.** J. 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Vis. Image Underst. 2017 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1607.05910.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FWu2017Visual.md)\n\n    *Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony R. Dick, Anton van den Hengel*\n\n38. **Visual Question Answering: Datasets, Algorithms, and Future Challenges.** Comput. Vis. Image Underst. 2017 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1610.01465.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FKafle2017Visual.md)\n\n    *Kushal Kafle, Christopher Kanan*\n\n39. **VLP: A Survey on Vision-Language Pre-training.** Int. J. Autom. Comput. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.09061.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FChen2023VLP.md)\n\n    *Feilong Chen, Duzhen Zhang, Minglun Han, Xiu-Yi Chen, Jing Shi, Shuang Xu, Bo Xu*\n\n#### [Summarization](#content)\n\n1. **A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization.** IEEE Access 2021 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9328413\u002F) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FSyed2021A.md)\n\n    *Ayesha Ayub Syed, Ford Lumban Gaol, Tokuro Matsuo*\n\n2. **A Survey on Cross-Lingual Summarization.** Trans. Assoc. Comput. Linguistics 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12515.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FWang2022A.md)\n\n    *Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie Zhou*\n\n3. **A Survey on Dialogue Summarization: Recent Advances and New Frontiers.** IJCAI 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.03175) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FFeng2022A.md)\n\n    *Xiachong Feng, Xiaocheng Feng, Bing Qin*\n\n4. **A Survey on Neural Network-Based Summarization Methods.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04589) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FDong2018A.md)\n\n    *Yue Dong*\n\n5. **Abstractive Meeting Summarization: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.04163.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FRennard2022Abstractive.md)\n\n    *Virgile Rennard, Guokan Shang, Julie Hunter, Michalis Vazirgiannis*\n\n6. **Abstractive Summarization: A Survey of the State of the Art.** AAAI 2019 [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F5056) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FLin2019Abstractive.md)\n\n    *Hui Lin, Vincent Ng*\n\n7. **Automated text summarisation and evidence-based medicine: A survey of two domains.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.08162) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FSarker2017Automated.md)\n\n    *Abeed Sarker, Diego Mollá Aliod, Cécile Paris*\n\n8. **Automatic Keyword Extraction for Text Summarization: A Survey.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.03242) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FBharti2017Automatic.md)\n\n    *Santosh Kumar Bharti, Korra Sathya Babu*\n\n9. **Automatic summarization of scientific articles: A survey.** J. King Saud Univ. Comput. Inf. Sci. 2022 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1319157820303554) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FAltmami2022Automatic.md)\n\n    *Nouf Ibrahim Altmami, Mohamed El Bachir Menai*\n\n10. **Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges.** Mathematical Problems in Engineering 2020 [paper](https:\u002F\u002Fwww.hindawi.com\u002Fjournals\u002Fmpe\u002F2020\u002F9365340\u002F) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FSuleiman2020Deep.md)\n\n    *Dima Suleiman, Arafat Awajan*\n\n11. **From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information.** IJCAI 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.04684) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FGao2020From.md)\n\n    *Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan*\n\n12. **How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation.** EACL 2021 [paper](https:\u002F\u002Faclanthology.org\u002F2021.eacl-main.160.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FSteen2021How.md)\n\n    *Julius Steen, Katja Markert*\n\n13. **Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures.** Knowl. Based Syst. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.11190.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FQu2022Knowledge-aware.md)\n\n    *Yutong Qu, Wei Emma Zhang, Jian Yang, Lingfei Wu, Jia Wu*\n\n14. **Multi-document Summarization via Deep Learning Techniques: A Survey.** ACM Comput. Surv. 2023 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.04843.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FMa2023Multi-document.md)\n\n    *Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, Quan Z. Sheng*\n\n15. **Neural Abstractive Text Summarization with Sequence-to-Sequence Models.** Trans. Data Sci. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.02303) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FShi2021Neural.md)\n\n    *Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy*\n\n16. **Recent automatic text summarization techniques: a survey.** Artif. Intell. Rev. 2017 [paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007%2Fs10462-016-9475-9) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FGambhir2017Recent.md)\n\n    *Mahak Gambhir, Vishal Gupta*\n\n17. **Text Summarization Techniques: A Brief Survey.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.02268) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FAllahyari2017Text.md)\n\n    *Mehdi Allahyari, Seyed Amin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys J. Kochut*\n\n18. **The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.14839) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FHuang2021The.md)\n\n    *Yi-Chong Huang, Xia-Chong Feng, Xiao-Cheng Feng, Bing Qin*\n\n19. **What Have We Achieved on Text Summarization?.** EMNLP 2020 [paper](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.33.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FHuang2020What.md)\n\n    *Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, Yue Zhang*\n\n#### [Tagging, Chunking, Syntax and Parsing](#content)\n\n1. **A survey of cross-lingual features for zero-shot cross-lingual semantic parsing.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.10461) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FYang2019A.md)\n\n    *Jingfeng Yang, Federico Fancellu, Bonnie L. Webber*\n\n2. **A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures.** arXiv 2020 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.11056.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FZhang2020A.md)\n\n    *Meishan Zhang*\n\n3. **A Survey on Recent Advances in Sequence Labeling from Deep Learning Models.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.06727) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FHe2020A.md)\n\n    *Zhiyong He, Zanbo Wang, Wei Wei, Shanshan Feng, Xianling Mao, Sheng Jiang*\n\n4. **A Survey on Semantic Parsing.** AKBC 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.00978) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FKamath2019A.md)\n\n    *Aishwarya Kamath, Rajarshi Das*\n\n5. **A Survey on Semantic Parsing from the perspective of Compositionality.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.14116.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FKumar2020A.md)\n\n    *Pawan Kumar, Srikanta Bedathur*\n\n6. **A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.13629.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FQin2022A.md)\n\n    *Bowen Qin, Binyuan Hui, Lihan Wang, Min Yang, Jinyang Li, Binhua Li, Ruiying Geng, Rongyu Cao, Jian Sun, Luo Si, Fei Huang, Yongbin Li*\n\n7. **Context Dependent Semantic Parsing: A Survey.** COLING 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.00797.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FLi2020Context.md)\n\n    *Zhuang Li, Lizhen Qu, Gholamreza Haffari*\n\n8. **Design Challenges and Misconceptions in Neural Sequence Labeling.** COLING 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.04470) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FYang2018Design.md)\n\n    *Jie Yang, Shuailong Liang, Yue Zhang*\n\n9. **Part‐of‐speech tagging.** Wiley Interdisciplinary Reviews: Computational Statistics 2011 [paper](https:\u002F\u002Fwires.onlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1002\u002Fwics.195) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FMartinez2011Part‐of‐speech.md)\n\n    *Angel R. Martinez*\n\n10. **Sememe knowledge computation: a review of recent advances in application and expansion of sememe knowledge bases.** Frontiers Comput. Sci. 2021 [paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11704-020-0002-4) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FQi2021Sememe.md)\n\n    *Fanchao Qi, Ruobing Xie, Yuan Zang, Zhiyuan Liu, Maosong Sun*\n\n11. **Syntactic Parsing: A Survey.** Computers and the Humanities 1989 [paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF00058766) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FSanders1989Syntactic.md)\n\n    *Alton F. Sanders and Ruth H. Sanders*\n\n12. **Syntax Representation in Word Embeddings and Neural Networks - A Survey.** ITAT 2020 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.01063.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FLimisiewicz2020Syntax.md)\n\n    *Tomasz Limisiewicz, David Marecek*\n\n13. **The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers.** IEEE Trans. Pattern Anal. Mach. Intell. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07290) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FZhang2020The.md)\n\n    *Dongxiang Zhang, Lei Wang, Luming Zhang, Bing Tian Dai, Heng Tao Shen*\n\n#### [Text Classification](#content)\n\n1. **A Survey of Active Learning for Text Classification using Deep Neural Networks.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.07267) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FSchröder2020A.md)\n\n    *Christopher Schröder, Andreas Niekler*\n\n2. **A Survey of Naïve Bayes Machine Learning approach in Text Document Classification.** arXiv 2010 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1003.1795) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FVidhya2010A.md)\n\n    *K. A. Vidhya, G. Aghila*\n\n3. **A Survey on Data Augmentation for Text Classification.** ACM Comput. Surv. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.03158) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FBayer2023A.md)\n\n    *Markus Bayer, Marc-André Kaufhold, Christian Reuter*\n\n4. **A Survey on Natural Language Processing for Fake News Detection.** LREC 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.00770) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FOshikawa2020A.md)\n\n    *Ray Oshikawa, Jing Qian, William Yang Wang*\n\n5. **A survey on phrase structure learning methods for text classification.** arXiv 2014 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.5598) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FPrasad2014A.md)\n\n    *Reshma Prasad, Mary Priya Sebastian*\n\n6. **A Survey on Stance Detection for Mis- and Disinformation Identification.** NAACL-HLT 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00242) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FHardalov2022A.md)\n\n    *Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein*\n\n7. **A Survey on Text Classification: From Shallow to Deep Learning.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2008.00364.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FLi2020A.md)\n\n    *Qian Li, Hao Peng, Jianxin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He*\n\n8. **Automatic Language Identification in Texts: A Survey.** J. Artif. Intell. Res. 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08186) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FJauhiainen2019Automatic.md)\n\n    *Tommi Jauhiainen, Marco Lui, Marcos Zampieri, Timothy Baldwin, Krister Lindén*\n\n9. **Deep Learning-based Text Classification: A Comprehensive Review.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.03705) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FMinaee2022Deep.md)\n\n    *Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao*\n\n10. **Fake News Detection using Stance Classification: A Survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.00181) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FLillie2019Fake.md)\n\n    *Anders Edelbo Lillie, Emil Refsgaard Middelboe*\n\n11. **Out-of-Distribution Generalization in Text Classification: Past, Present, and Future.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14104) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FYang2023Out-of-Distribution.md)\n\n    *Linyi Yang, Yaoxiao Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang*\n\n12. **Semantic text classification: A survey of past and recent advances.** Inf. Process. Manag. 2018 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0306457317305757) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FAltinel2018Semantic.md)\n\n    *Berna Altinel, Murat Can Ganiz*\n\n13. **Text Classification Algorithms: A Survey.** Inf. 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.08067) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FKowsari2019Text.md)\n\n    *Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura E. Barnes, Donald E. Brown*\n\n## The ML Paper List\n\n#### [Architectures](#content)\n\n1. **A General Survey on Attention Mechanisms in Deep Learning.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.14263.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FBrauwers2022A.md)\n\n    *Gianni Brauwers, Flavius Frasincar*\n\n2. **A Practical Survey on Faster and Lighter Transformers.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.14636.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FFournier2021A.md)\n\n    *Quentin Fournier, Gaétan Marceau Caron, Daniel Aloise*\n\n3. **A Review of Binarized Neural Networks.** Electronics 2019 [paper](http:\u002F\u002Fwww.socolar.com\u002FArticle\u002FIndex?aid=100010075063&jid=100000022108) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FSimons2019A.md)\n\n    *Taylor Simons, Dah-Jye Lee*\n\n4. **A Review of Sparse Expert Models in Deep Learning.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.01667.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FFedus2022A.md)\n\n    *William Fedus, Jeff Dean, Barret Zoph*\n\n5. **A State-of-the-Art Survey on Deep Learning Theory and Architectures.** Electronics 2019 [paper](https:\u002F\u002Fwww.mdpi.com\u002F2079-9292\u002F8\u002F3\u002F292) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FAlom2019A.md)\n\n    *Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal and Vijayan K. Asari*\n\n6. **A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.02806) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FLi2020A.md)\n\n    *Zewen Li, Wenjie Yang, Shouheng Peng, Fan Liu*\n\n7. **A Survey of End-to-End Driving: Architectures and Training Methods.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.06404) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FTampuu2020A.md)\n\n    *Ardi Tampuu, Maksym Semikin, Naveed Muhammad, Dmytro Fishman, Tambet Matiisen*\n\n8. **A survey of the recent architectures of deep convolutional neural networks.** Artif. Intell. Rev. 2020 [paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10462-020-09825-6) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FKhan2020A.md)\n\n    *Asifullah Khan, Anabia Sohail, Umme Zahoora, Aqsa Saeed Qureshi*\n\n9. **A Survey of Transformers.** AI Open 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.04554.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FLin2022A.md)\n\n    *Tianyang Lin, Yuxin Wang, Xiangyang Liu, Xipeng Qiu*\n\n10. **A Survey on Activation Functions and their relation with Xavier and He Normal Initialization.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.06632) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FDatta2020A.md)\n\n    *Leonid Datta*\n\n11. **A Survey on Latent Tree Models and Applications.** J. Artif. Intell. Res. 2013 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1402.0577) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FMourad2013A.md)\n\n    *Raphaël Mourad, Christine Sinoquet, Nevin Lianwen Zhang, Tengfei Liu, Philippe Leray*\n\n12. **A survey on modern trainable activation functions.** Neural Networks 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.00817) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FApicella2021A.md)\n\n    *Andrea Apicella, Francesco Donnarumma, Francesco Isgrò, Roberto Prevete*\n\n13. **A Survey on Vision Transformer.** IEEE Trans. Pattern Anal. Mach. Intell. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.12556) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FHan2023A.md)\n\n    *Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, Dacheng Tao*\n\n14. **An Attentive Survey of Attention Models.** ACM Trans. Intell. Syst. Technol. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.02874) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FChaudhari2021An.md)\n\n    *Sneha Chaudhari, Varun Mithal, Gungor Polatkan, Rohan Ramanath*\n\n15. **An Introduction to Autoencoders.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.03898.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FMichelucci2022An.md)\n\n    *Umberto Michelucci*\n\n16. **Attention mechanisms and deep learning for machine vision: A survey of the state of the art.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.07550.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FHafiz2021Attention.md)\n\n    *Abdul Mueed Hafiz, Shabir Ahmad Parah, Rouf Ul Alam Bhat*\n\n17. **Attention Mechanisms in Computer Vision: A Survey.** Comput. Vis. Media 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.07624.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FGuo2022Attention.md)\n\n    *Meng-Hao Guo, Tian-Xing Xu, Jiang-Jiang Liu, Zheng-Ning Liu, Peng-Tao Jiang, Tai-Jiang Mu, Song-Hai Zhang, Ralph R. Martin, Ming-Ming Cheng, Shi-Min Hu*\n\n18. **Big Networks: A Survey.** Comput. Sci. Rev. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.03638) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FBedru2020Big.md)\n\n    *Hayat Dino Bedru, Shuo Yu, Xinru Xiao, Da Zhang, Liangtian Wan, He Guo, Feng Xia*\n\n19. **Binary Neural Networks: A Survey.** Pattern Recognit. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.03333) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FQin2020Binary.md)\n\n    *Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, Nicu Sebe*\n\n20. **Deep Echo State Network (DeepESN): A Brief Survey.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.04323) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FGallicchio2017Deep.md)\n\n    *Claudio Gallicchio, Alessio Micheli*\n\n21. **Deep Tree Transductions - A Short Survey.** INNSBDDL 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.01737) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FBacciu2019Deep.md)\n\n    *Davide Bacciu, Antonio Bruno*\n\n22. **Efficient Transformers: A Survey.** ACM Comput. Surv. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.06732) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FTay2023Efficient.md)\n\n    *Yi Tay, Mostafa Dehghani, Dara Bahri, Donald Metzler*\n\n23. **Learning with Capsules: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.02664.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FRibeiro2022Learning.md)\n\n    *Fabio De Sousa Ribeiro, Kevin Duarte, Miles Everett, Georgios Leontidis, Mubarak Shah*\n\n24. **On the Opportunity of Causal Deep Generative Models: A Survey and Future Directions.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.12351.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FZhou2023On.md)\n\n    *Guanglin Zhou, Lina Yao, Xiwei Xu, Chen Wang, Liming Zhu, Kun Zhang*\n\n25. **Pooling Methods in Deep Neural Networks, a Review.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07485) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FGholamalinezhad2020Pooling.md)\n\n    *Hossein Gholamalinezhad, Hossein Khosravi*\n\n26. **Position Information in Transformers: An Overview.** Comput. Linguistics 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.11090) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FDufter2022Position.md)\n\n    *Philipp Dufter, Martin Schmitt, Hinrich Schütze*\n\n27. **Recent Advances in Convolutional Neural Networks.** Pattern Recognit. 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.07108) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FGu2018Recent.md)\n\n    *Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, Tsuhan Chen*\n\n28. **Sum-Product Networks: A Survey.** arXiv 2020 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9363463) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FParís2020Sum-Product.md)\n\n    *Iago París, Raquel Sánchez-Cauce, Francisco Javier Díez*\n\n29. **Survey of Dropout Methods for Deep Neural Networks.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.13310) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FLabach2019Survey.md)\n\n    *Alex Labach, Hojjat Salehinejad, Shahrokh Valaee*\n\n30. **Survey on the attention based RNN model and its applications in computer vision.** arXiv 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.06823) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FWang2016Survey.md)\n\n    *Feng Wang, David M. J. Tax*\n\n31. **The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01164) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FAlom2018The.md)\n\n    *Md. Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari*\n\n32. **The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures.** IEEE Access 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.10640.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FSingh2021The.md)\n\n    *Sushant Singh, Ausif Mahmood*\n\n33. **Transformers in Vision: A Survey.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.01169) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FKhan2022Transformers.md)\n\n    *Salman H. Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, Mubarak Shah*\n\n34. **Understanding LSTM - a tutorial into Long Short-Term Memory Recurrent Neural Networks.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.09586) [bib](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FStaudemeyer2019Understanding.md)\n\n    *Ralf C. Staudemeyer, Eric Rothstein Morris*\n\n#### [AutoML](#content)\n\n1. **A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.02903) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FRen2022A.md)\n\n    *Pengzhen Ren, Yun Xiao, Xiaojun Chang, Poyao Huang, Zhihui Li, Xiaojiang Chen, Xin Wang*\n\n2. **A Comprehensive Survey on Automated Machine Learning for Recommendations.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.01390) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FChen2022A.md)\n\n    *Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, Ruiming Tang*\n\n3. **A Comprehensive Survey on Hardware-Aware Neural Architecture Search.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.09336) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FBenmeziane2021A.md)\n\n    *Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang*\n\n4. **A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.07995.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FJaâfra2018A.md)\n\n    *Yesmina Jaâfra, Jean Luc Laurent, Aline Deruyver, Mohamed Saber Naceur*\n\n5. **A Survey on Neural Architecture Search.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.01392) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FWistuba2019A.md)\n\n    *Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati*\n\n6. **Automated Machine Learning on Graphs: A Survey.** IJCAI 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00742) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FZhang2021Automated.md)\n\n    *Ziwei Zhang, Xin Wang, Wenwu Zhu*\n\n7. **AutoML for Deep Recommender Systems: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.13922.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FZheng2022AutoML.md)\n\n    *Ruiqi Zheng, Liang Qu, Bin Cui, Yuhui Shi, Hongzhi Yin*\n\n8. **AutoML: A Survey of the State-of-the-Art.** Knowl. Based Syst. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.00709) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FHe2021AutoML.md)\n\n    *Xin He, Kaiyong Zhao, Xiaowen Chu*\n\n9. **Benchmark and Survey of Automated Machine Learning Frameworks.** J. Artif. Intell. Res. 2021 [paper](https:\u002F\u002Fwww.jair.org\u002Findex.php\u002Fjair\u002Farticle\u002Fview\u002F11854) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FZöller2021Benchmark.md)\n\n    *Marc-André Zöller, Marco F. Huber*\n\n10. **Neural Architecture Search: A Survey.** J. Mach. Learn. Res. 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05377) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FElsken2019Neural.md)\n\n    *Thomas Elsken, Jan Hendrik Metzen, Frank Hutter*\n\n11. **Reinforcement learning for neural architecture search: A review.** Image Vis. Comput. 2019 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0262885619300885?via%3Dihub) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FJaâfra2019Reinforcement.md)\n\n    *Yesmina Jaâfra, Jean Luc Laurent, Aline Deruyver, Mohamed Saber Naceur*\n\n12. **Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.10658.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FLi2022Survey.md)\n\n    *Nan Li, Lianbo Ma, Guo Yu, Bing Xue, Mengjie Zhang, Yaochu Jin*\n\n#### [Bayesian Methods](#content)\n\n1. **A survey of non-exchangeable priors for Bayesian nonparametric models.** IEEE Trans. Pattern Anal. Mach. Intell. 2015 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1211.4798) [bib](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FFoti2015A.md)\n\n    *Nicholas J. Foti, Sinead A. Williamson*\n\n2. **A Survey on Bayesian Deep Learning.** ACM Comput. Surv. 2021 [paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F1604.01662) [bib](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FWang2021A.md)\n\n    *Hao Wang, Dit-Yan Yeung*\n\n3. **Bayesian Neural Networks: An Introduction and Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.12024) [bib](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FGoan2020Bayesian.md)\n\n    *Ethan Goan, Clinton Fookes*\n\n4. **Bayesian Nonparametric Space Partitions: A Survey.** IJCAI 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11394) [bib](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FFan2021Bayesian.md)\n\n    *Xuhui Fan, Bin Li, Ling Luo, Scott A. Sisson*\n\n5. **Deep Bayesian Active Learning, A Brief Survey on Recent Advances.** arXiv 2020 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.08044.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FMohamadi2020Deep.md)\n\n    *Salman Mohamadi, Hamidreza Amindavar*\n\n6. **Hands-on Bayesian Neural Networks - a Tutorial for Deep Learning Users.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.06823) [bib](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FJospin2020Hands-on.md)\n\n    *Laurent Valentin Jospin, Wray L. Buntine, Farid Boussaïd, Hamid Laga, Mohammed Bennamoun*\n\n7. **Taking the Human Out of the Loop: A Review of Bayesian Optimization.** Proc. IEEE 2016 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=7352306) [bib](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FShahriari2016Taking.md)\n\n    *Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas*\n\n#### [Classification, Clustering and Regression](#content)\n\n1. **A continual learning survey: Defying forgetting in classification tasks.** IEEE Trans. Pattern Anal. Mach. Intell. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.08383.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FLange2022A.md)\n\n    *Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory G. Slabaugh, Tinne Tuytelaars*\n\n2. **A Survey of Classification Techniques in the Area of Big Data.** arXiv 2015 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1503.07477) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FKoturwar2015A.md)\n\n    *Praful Koturwar, Sheetal Girase, Debajyoti Mukhopadhyay*\n\n3. **A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09319) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FSwiler2020A.md)\n\n    *Laura P. Swiler, Mamikon Gulian, Ari Frankel, Cosmin Safta, John D. Jakeman*\n\n4. **A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.12875.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FLiu2022A.md)\n\n    *Yue Liu, Jun Xia, Sihang Zhou, Siwei Wang, Xifeng Guo, Xihong Yang, Ke Liang, Wenxuan Tu, Stan Z. Li, Xinwang Liu*\n\n5. **A Survey of Machine Learning Methods and Challenges for Windows Malware Classification.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09271) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FRaff2020A.md)\n\n    *Edward Raff, Charles Nicholas*\n\n6. **A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11870) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FHasib2020A.md)\n\n    *Khan Md. Hasib, Md. Sadiq Iqbal, Faisal Muhammad Shah, Jubayer Al Mahmud, Mahmudul Hasan Popel, Md. Imran Hossain Showrov, Shakil Ahmed, Obaidur Rahman*\n\n7. **A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.04791.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FGhods2019A.md)\n\n    *Alireza Ghods, Diane J. Cook*\n\n8. **A Survey on Multi-View Clustering.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.06246) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FChao2017A.md)\n\n    *Guoqing Chao, Shiliang Sun, Jinbo Bi*\n\n9. **Comprehensive Comparative Study of Multi-Label Classification Methods.** Expert Syst. Appl. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07113) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FBogatinovski2022Comprehensive.md)\n\n    *Jasmin Bogatinovski, Ljupco Todorovski, Saso Dzeroski, Dragi Kocev*\n\n10. **Deep Clustering: A Comprehensive Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.04142.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FRen2022Deep.md)\n\n    *Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S. Yu, Lifang He*\n\n11. **Deep learning for time series classification: a review.** Data Min. Knowl. Discov. 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04356) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FFawaz2019Deep.md)\n\n    *Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller*\n\n12. **How Complex is your classification problem? A survey on measuring classification complexity.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.03591) [bib](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FLorena2018How.md)\n\n    *Ana Carolina Lorena, Luís Paulo F. Garcia, Jens Lehmann, Marcilio C. P. de Souto, Tin Kam Ho*\n\n#### [Computer Vision](#content)\n\n1. **3D Human Motion Prediction: A Survey.** Neurocomputing 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.01593) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FLyu20223D.md)\n\n    *Kedi Lyu, Haipeng Chen, Zhenguang Liu, Beiqi Zhang, Ruili Wang*\n\n2. **3D Object Detection for Autonomous Driving: A Survey.** Pattern Recognit. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.10823.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FQian20223D.md)\n\n    *Rui Qian, Xin Lai, Xirong Li*\n\n3. **3D Object Detection from Images for Autonomous Driving: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.02980.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FMa20223D.md)\n\n    *Xinzhu Ma, Wanli Ouyang, Andrea Simonelli, Elisa Ricci*\n\n4. **3D Vision with Transformers: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.04309.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FLahoud20223D.md)\n\n    *Jean Lahoud, Jiale Cao, Fahad Shahbaz Khan, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Ming-Hsuan Yang*\n\n5. **A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.06544.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FYang2022A.md)\n\n    *Zihan Yang, Richard O. Sinnott, James Bailey, Qiuhong Ke*\n\n6. **A Survey of Black-Box Adversarial Attacks on Computer Vision Models.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.01667.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FBhambri2019A.md)\n\n    *Siddhant Bhambri, Sumanyu Muku, Avinash Tulasi, Arun Balaji Buduru*\n\n7. **A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.02831) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FWang2022A.md)\n\n    *Tao Wang, Kaihao Zhang, Xuanxi Chen, Wenhan Luo, Jiankang Deng, Tong Lu, Xiaochun Cao, Wei Liu, Hongdong Li, Stefanos Zafeiriou*\n\n8. **A survey of loss functions for semantic segmentation.** CIBCB 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.14822.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FJadon2020A.md)\n\n    *Shruti Jadon*\n\n9. **A Survey of Modern Deep Learning based Object Detection Models.** Digit. Signal Process. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.11892.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZaidi2022A.md)\n\n    *Syed Sahil Abbas Zaidi, Mohammad Samar Ansari, Asra Aslam, Nadia Kanwal, Mamoona Naveed Asghar, Brian Lee*\n\n10. **A survey of top-down approaches for human pose estimation.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.02656.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FNguyen2022A.md)\n\n    *Thong Duy Nguyen, Milan Kresovic*\n\n11. **A Survey of Vision-Language Pre-Trained Models.** IJCAI 2022 [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0762.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FDu2022A.md)\n\n    *Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao*\n\n12. **A Survey of Visual Sensory Anomaly Detection.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.07006.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FJiang2022A.md)\n\n    *Xi Jiang, Guoyang Xie, Jinbao Wang, Yong Liu, Chengjie Wang, Feng Zheng, Yaochu Jin*\n\n13. **A Survey of Visual Transformers.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.06091.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FLiu2021A.md)\n\n    *Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao Shi, Jianping Fan, Zhiqiang He*\n\n14. **A survey on applications of augmented, mixed and virtual reality for nature and environment.** HCI 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.12024) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FRambach2021A.md)\n\n    *Jason R. Rambach, Gergana Lilligreen, Alexander Schäfer, Ramya Bankanal, Alexander Wiebel, Didier Stricker*\n\n15. **A survey on deep hashing for image retrieval.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.05627) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZhang2020A.md)\n\n    *Xiaopeng Zhang*\n\n16. **A Survey on Deep Learning in Medical Image Analysis.** Medical Image Anal. 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.05747) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FLitjens2017A.md)\n\n    *Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W. M. van der Laak, Bram van Ginneken, Clara I. Sánchez*\n\n17. **A Survey on Deep Learning Technique for Video Segmentation.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.01153.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FWang2021A.md)\n\n    *Wenguan Wang, Tianfei Zhou, Fatih Porikli, David J. Crandall, Luc Van Gool*\n\n18. **A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.13232.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FChen2022A.md)\n\n    *Chaoqi Chen, Yushuang Wu, Qiyuan Dai, Hong-Yu Zhou, Mutian Xu, Sibei Yang, Xiaoguang Han, Yizhou Yu*\n\n19. **A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction.** IEEE Trans. Pattern Anal. Mach. Intell. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.01223) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FShen2022A.md)\n\n    *Wei Shen, Zelin Peng, Xuehui Wang, Huayu Wang, Jiazhong Cen, Dongsheng Jiang, Lingxi Xie, Xiaokang Yang, Qi Tian*\n\n20. **A Survey on Visual Map Localization Using LiDARs and Cameras.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.03376.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FElhousni2022A.md)\n\n    *Mahdi Elhousni, Xinming Huang*\n\n21. **A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation.** ICCVW 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.09522.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZhao2021A.md)\n\n    *Yiming Zhao, Xiao Zhang, Xinming Huang*\n\n22. **Advances in adversarial attacks and defenses in computer vision: A survey.** IEEE Access 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.00401.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FAkhtar2021Advances.md)\n\n    *Naveed Akhtar, Ajmal Mian, Navid Kardan, Mubarak Shah*\n\n23. **Adversarial Examples on Object Recognition: A Comprehensive Survey.** ACM Comput. Surv. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.04094) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FSerban2021Adversarial.md)\n\n    *Alexandru Constantin Serban, Erik Poll, Joost Visser*\n\n24. **Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.03728.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FMachado2020Adversarial.md)\n\n    *Gabriel Resende Machado, Eugênio Silva, Ronaldo Ribeiro Goldschmidt*\n\n25. **Affective Image Content Analysis: Two Decades Review and New Perspectives.** IEEE Trans. Pattern Anal. Mach. Intell. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.16125.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZhao2022Affective.md)\n\n    *Sicheng Zhao, Xingxu Yao, Jufeng Yang, Guoli Jia, Guiguang Ding, Tat-Seng Chua, Björn W. Schuller, Kurt Keutzer*\n\n26. **Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.00358.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZhang2021Applications.md)\n\n    *Jinghua Zhang, Chen Li, Marcin Grzegorzek*\n\n27. **Automatic Gaze Analysis: A Survey of Deep Learning based Approaches.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.05479.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FGhosh2021Automatic.md)\n\n    *Shreya Ghosh, Abhinav Dhall, Munawar Hayat, Jarrod Knibbe, Qiang Ji*\n\n28. **Bridging Gap between Image Pixels and Semantics via Supervision: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.13757.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FDuan2021Bridging.md)\n\n    *Jiali Duan, C.-C. Jay Kuo*\n\n29. **Compositional Scene Representation Learning via Reconstruction: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.07135.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FYuan2022Compositional.md)\n\n    *Jinyang Yuan, Tonglin Chen, Bin Li, Xiangyang Xue*\n\n30. **Deep Depth Completion from Extremely Sparse Data: A Survey.** IEEE Trans. Pattern Anal. Mach. Intell. 2022 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9984942 ) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FHu2022Deep.md)\n\n    *Junjie Hu, Chenyu Bao, Mete Ozay, Chenyou Fan, Qing Gao, Honghai Liu, Tin Lun Lam*\n\n31. **Deep Image Deblurring: A Survey.** Int. J. Comput. Vis. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.10700.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZhang2022Deep.md)\n\n    *Kaihao Zhang, Wenqi Ren, Wenhan Luo, Wei-Sheng Lai, Björn Stenger, Ming-Hsuan Yang, Hongdong Li*\n\n32. **Deep Learning for 3D Point Cloud Understanding: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08920) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FLu2020Deep.md)\n\n    *Haoming Lu, Humphrey Shi*\n\n33. **Deep Learning for Embodied Vision Navigation: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.04097.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZhu2021Deep.md)\n\n    *Fengda Zhu, Yi Zhu, Xiaodan Liang, Xiaojun Chang*\n\n34. **Deep Learning for Image Super-resolution: A Survey.** IEEE Trans. Pattern Anal. Mach. Intell. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.06068) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FWang2021Deep.md)\n\n    *Zhihao Wang, Jian Chen, Steven C. H. Hoi*\n\n35. **Deep Learning for Instance Retrieval: A Survey.** IEEE Trans. Pattern Anal. Mach. Intell. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.11282.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FChen2021Deep.md)\n\n    *Wei Chen, Yu Liu, Weiping Wang, Erwin Bakker, Theodoros Georgiou, Paul Fieguth, Li Liu, Michael S. Lew*\n\n36. **Deep Learning for Scene Classification: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.10531) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZeng2021Deep.md)\n\n    *Delu Zeng, Minyu Liao, Mohammad Tavakolian, Yulan Guo, Bolei Zhou, Dewen Hu, Matti Pietikäinen, Li Liu*\n\n37. **Deep Learning Technique for Human Parsing: A Survey and Outlook.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.00394.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FYang2023Deep.md)\n\n    *Lu Yang, Wenhe Jia, Shan Li, Qing Song*\n\n38. **Efficient High-Resolution Deep Learning: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.13050.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FBakhtiarnia2022Efficient.md)\n\n    *Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis*\n\n39. **Geometric and Learning-based Mesh Denoising: A Comprehensive Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.00841.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FChen2022Geometric.md)\n\n    *Honghua Chen, Mingqiang Wei, Jun Wang*\n\n40. **Image Segmentation Using Deep Learning: A Survey.** IEEE Trans. Pattern Anal. Mach. Intell. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2001.05566.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FMinaee2022Image.md)\n\n    *Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, Demetri Terzopoulos*\n\n41. **Image\u002FVideo Deep Anomaly Detection: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.01739) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FMohammadi2021Image.md)\n\n    *Bahram Mohammadi, Mahmood Fathy, Mohammad Sabokrou*\n\n42. **Image-to-Image Translation: Methods and Applications.** IEEE Trans. Multim. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08629) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FPang2022Image-to-Image.md)\n\n    *Yingxue Pang, Jianxin Lin, Tao Qin, Zhibo Chen*\n\n43. **Imbalance Problems in Object Detection: A Review.** IEEE Trans. Pattern Anal. Mach. Intell. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.00169) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FOksuz2021Imbalance.md)\n\n    *Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas*\n\n44. **MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review.** Sensors 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.03004.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FWei2022MmWave.md)\n\n    *Zhiqing Wei, Fengkai Zhang, Shuo Chang, Yangyang Liu, Huici Wu, Zhiyong Feng*\n\n45. **Multi-modal Sensor Fusion for Auto Driving Perception: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.02703.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FHuang2022Multi-modal.md)\n\n    *Keli Huang, Botian Shi, Xiang Li, Xin Li, Siyuan Huang, Yikang Li*\n\n46. **Object Detection in 20 Years: A Survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.05055) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZou2019Object.md)\n\n    *Zhengxia Zou, Zhenwei Shi, Yuhong Guo, Jieping Ye*\n\n47. **Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01536.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FIslam2022Recent.md)\n\n    *Khawar Islam*\n\n48. **Recovering 3D Human Mesh from Monocular Images: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01923.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FTian2022Recovering.md)\n\n    *Yating Tian, Hongwen Zhang, Yebin Liu, Limin Wang*\n\n49. **Single Image Super-Resolution Methods: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.11763.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FMaral2022Single.md)\n\n    *Bahattin Can Maral*\n\n50. **Temporal Sentence Grounding in Videos: A Survey and Future Directions.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.08071.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZhang2022Temporal.md)\n\n    *Hao Zhang, Aixin Sun, Wei Jing, Joey Tianyi Zhou*\n\n51. **The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.13290.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FDu2022The.md)\n\n    *Hang Du, Hailin Shi, Dan Zeng, Xiao-Ping Zhang, Tao Mei*\n\n52. **The Impact of Machine Learning on 2D\u002F3D Registration for Image-guided Interventions: A Systematic Review and Perspective.** Frontiers Robotics AI 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.02238.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FUnberath2021The.md)\n\n    *Mathias Unberath, Cong Gao, Yicheng Hu, Max Judish, Russell H. Taylor, Mehran Armand, Robert B. Grupp*\n\n53. **The Need and Status of Sea Turtle Conservation and Survey of Associated Computer Vision Advances.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.14061.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FPaul2021The.md)\n\n    *Aditya Jyoti Paul*\n\n54. **Transformers in Remote Sensing: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.01206.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FAleissaee2022Transformers.md)\n\n    *Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal, Gui-Song Xia, Fahad Shahbaz Khan*\n\n55. **Transformers Meet Visual Learning Understanding: A Comprehensive Review.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12944.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FYang2022Transformers.md)\n\n    *Yuting Yang, Licheng Jiao, Xu Liu, Fang Liu, Shuyuan Yang, Zhixi Feng, Xu Tang*\n\n56. **Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10412.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FXu2022Video.md)\n\n    *Yuecong Xu, Haozhi Cao, Zhenghua Chen, Xiaoli Li, Lihua Xie, Jianfei Yang*\n\n#### [Contrastive Learning](#content)\n\n1. **A Survey on Contrastive Self-supervised Learning.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.00362) [bib](\u002Fbib\u002FMachine-Learning\u002FContrastive-Learning\u002FJaiswal2020A.md)\n\n    *Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, Fillia Makedon*\n\n2. **Contrastive Representation Learning: A Framework and Review.** IEEE Access 2020 [paper](http:\u002F\u002Fdoras.dcu.ie\u002F25121\u002F1\u002FACCESS3031549.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FContrastive-Learning\u002FLe-Khac2020Contrastive.md)\n\n    *Phuc H. Le-Khac, Graham Healy, Alan F. Smeaton*\n\n3. **Self-supervised Learning: Generative or Contrastive.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08218) [bib](\u002Fbib\u002FMachine-Learning\u002FContrastive-Learning\u002FLiu2020Self-supervised.md)\n\n    *Xiao Liu, Fanjin Zhang, Zhenyu Hou, Zhaoyu Wang, Li Mian, Jing Zhang, Jie Tang*\n\n#### [Curriculum Learning](#content)\n\n1. **A Survey on Curriculum Learning.** IEEE Trans. Pattern Anal. Mach. 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Vis. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.10382) [bib](\u002Fbib\u002FMachine-Learning\u002FCurriculum-Learning\u002FSoviany2022Curriculum.md)\n\n    *Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe*\n\n#### [Data Augmentation](#content)\n\n1. **A Comprehensive Survey of Dataset Distillation.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.05603) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FLei2023A.md)\n\n    *Shiye Lei, Dacheng Tao*\n\n2. **A Comprehensive Survey of Image Augmentation Techniques for Deep Learning.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.01491.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FXu2022A.md)\n\n    *Mingle Xu, Sook Yoon, Alvaro Fuentes, Dong Sun Park*\n\n3. **A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.06544.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FYang2022A.md)\n\n    *Zihan Yang, Richard O. Sinnott, James Bailey, Qiuhong Ke*\n\n4. **A Survey of Data Augmentation Approaches for NLP.** ACL 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.03075.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FFeng2021A.md)\n\n    *Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard H. Hovy*\n\n5. **A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.10888.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FCao2022A.md)\n\n    *Chengtai Cao, Fan Zhou, Yurou Dai, Jianping Wang*\n\n6. **A survey on Image Data Augmentation for Deep Learning.** J. Big Data 2019 [paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1186\u002Fs40537-019-0197-0) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FShorten2019A.md)\n\n    *Connor Shorten, Taghi M. Khoshgoftaar*\n\n7. **An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.15951) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FIwana2020An.md)\n\n    *Brian Kenji Iwana, Seiichi Uchida*\n\n8. **Data Augmentation Approaches in Natural Language Processing: A Survey.** AI Open 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.01852.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FLi2022Data.md)\n\n    *Bohan Li, Yutai Hou, Wanxiang Che*\n\n9. **Data Augmentation on Graphs: A Technical Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.09970.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FZhou2022Data.md)\n\n    *Jiajun Zhou, Chenxuan Xie, Zhenyu Wen, Xiangyu Zhao, Qi Xuan*\n\n10. **Data Distillation: A Survey.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.04272.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FSachdeva2023Data.md)\n\n    *Noveen Sachdeva, Julian J. McAuley*\n\n11. **Dataset Distillation: A Comprehensive Review.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.07014.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FYu2023Dataset.md)\n\n    *Ruonan Yu, Songhua Liu, Xinchao Wang*\n\n12. **Time Series Data Augmentation for Deep Learning: A Survey.** IJCAI 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.12478) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FWen2021Time.md)\n\n    *Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, Huan Xu*\n\n#### [Deep Learning General Methods](#content)\n\n1. **A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.06367) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FChen2022A.md)\n\n    *Hang Chen, Keqing Du, Xinyu Yang, Chenguang Li*\n\n2. **A Survey of Deep Active Learning.** ACM Comput. 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Gupta, Xiaojiang Chen, Xin Wang*\n\n3. **A Survey of Deep Learning for Data Caching in Edge Network.** Informatics 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.07235) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FWang2020A.md)\n\n    *Yantong Wang, Vasilis Friderikos*\n\n4. **A Survey of Deep Learning for Mathematical Reasoning.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.10535.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FLu2022A.md)\n\n    *Pan Lu, Liang Qiu, Wenhao Yu, Sean Welleck, Kai-Wei Chang*\n\n5. **A Survey of Deep Learning for Scientific Discovery.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.11755.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FRaghu2020A.md)\n\n    *Maithra Raghu, Eric Schmidt*\n\n6. **A Survey of Label-noise Representation Learning: Past, Present and Future.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.04406.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FHan2020A.md)\n\n    *Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama*\n\n7. **A Survey of Neuromorphic Computing and Neural Networks in Hardware.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.06963) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FSchuman2017A.md)\n\n    *Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, James S. Plank*\n\n8. **A Survey of Uncertainty in Deep Neural Networks.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.03342.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FGawlikowski2021A.md)\n\n    *Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna M. Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, Xiao Xiang Zhu*\n\n9. **A Survey on Active Deep Learning: From Model-driven to Data-driven.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.09933) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FLiu2020A.md)\n\n    *Peng Liu, Lizhe Wang, Guojin He, Lei Zhao*\n\n10. **A Survey on Assessing the Generalization Envelope of Deep Neural Networks: Predictive Uncertainty, Out-of-distribution and Adversarial Samples.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.09381) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FLust2020A.md)\n\n    *Julia Lust, Alexandru Paul Condurache*\n\n11. **A Survey on Concept Factorization: From Shallow to Deep Representation Learning.** Inf. Process. 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Data Eng. 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.08752) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FCui2019A.md)\n\n    *Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu*\n\n16. **A Tutorial on Network Embeddings.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.02590) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FChen2018A.md)\n\n    *Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena*\n\n17. **Continual Lifelong Learning with Neural Networks: A Review.** Neural Networks 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.07569.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FParisi2019Continual.md)\n\n    *German Ignacio Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, Stefan Wermter*\n\n18. **Convergence of Edge Computing and Deep Learning: A Comprehensive Survey.** IEEE Commun. Surv. 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Intell. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.04906) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FHan2022Dynamic.md)\n\n    *Yizeng Han, Gao Huang, Shiji Song, Le Yang, Honghui Wang, Yulin Wang*\n\n24. **Embracing Change: Continual Learning in Deep Neural Networks.** Trends in Cognitive Sciences 2020 [paper](https:\u002F\u002Fwww.cell.com\u002Ftrends\u002Fcognitive-sciences\u002Ffulltext\u002FS1364-6613(20)30219-9?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1364661320302199%3Fshowall%3Dtrue) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FHadsell2020Embracing.md)\n\n    *Raia Hadsell, Dushyant Rao, Andrei A. Rusu, Razvan Pascanu*\n\n25. **Geometric deep learning: going beyond Euclidean data.** IEEE Signal Process. 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Inf. Syst. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.05127) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FHu2021Model.md)\n\n    *Xia Hu, Lingyang Chu, Jian Pei, Weiqing Liu, Jiang Bian*\n\n33. **Network representation learning: A macro and micro view.** AI Open 2021 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666651021000024) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FLiu2021Network.md)\n\n    *Xueyi Liu, Jie Tang*\n\n34. **Network Representation Learning: A Survey.** IEEE Trans. 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Courville, Pascal Vincent*\n\n42. **Review: Ordinary Differential Equations For Deep Learning.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.00502) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FChen2019Review.md)\n\n    *Xinshi Chen*\n\n43. **Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks.** J. Mach. Learn. Res. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.00554) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FHoefler2021Sparsity.md)\n\n    *Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste*\n\n44. **Survey of Expressivity in Deep Neural Networks.** arXiv 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.08083) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FRaghu2016Survey.md)\n\n    *Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein*\n\n45. **Survey of reasoning using Neural networks.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.06186) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FSahu2017Survey.md)\n\n    *Amit Sahu*\n\n46. **Survey on Large Scale Neural Network Training.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.10435.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FGusak2022Survey.md)\n\n    *Julia Gusak, Daria Cherniuk, Alena Shilova, Alexandr Katrutsa, Daniel Bershatsky, Xunyi Zhao, Lionel Eyraud-Dubois, Oleg Shlyazhko, Denis Dimitrov, Ivan V. Oseledets, Olivier Beaumont*\n\n47. **The Deep Learning Compiler: A Comprehensive Survey.** IEEE Trans. Parallel Distributed Syst. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.03794) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FLi2021The.md)\n\n    *Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Lin Gan, Guangwen Yang, Depei Qian*\n\n48. **The Modern Mathematics of Deep Learning.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.04026.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FBerner2021The.md)\n\n    *Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen*\n\n49. **Time Series Data Imputation: A Survey on Deep Learning Approaches.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.11347.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FFang2020Time.md)\n\n    *Chenguang Fang, Chen Wang*\n\n50. **Time Series Forecasting With Deep Learning: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.13408) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FLim2020Time.md)\n\n    *Bryan Lim, Stefan Zohren*\n\n51. **Tutorial on Variational Autoencoders.** arXiv 2016 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1606.05908.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FDoersch2016Tutorial.md)\n\n    *Carl Doersch*\n\n#### [Deep Reinforcement Learning](#content)\n\n1. **A Mini Review on the utilization of Reinforcement Learning with OPC UA.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15113) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FSchindler2023A.md)\n\n    *Simon Schindler, Martin Uray, Stefan Huber*\n\n2. **A Short Survey On Memory Based Reinforcement Learning.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.06736) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FRamani2019A.md)\n\n    *Dhruv Ramani*\n\n3. **A Short Survey on Probabilistic Reinforcement Learning.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.07010) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FRussel2019A.md)\n\n    *Reazul Hasan Russel*\n\n4. **A survey of benchmarking frameworks for reinforcement learning.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.13577.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FStapelberg2020A.md)\n\n    *Belinda Stapelberg, Katherine M. Malan*\n\n5. **A Survey of Explainable Reinforcement Learning.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.08434.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMilani2022A.md)\n\n    *Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang*\n\n6. **A Survey of Exploration Strategies in Reinforcement Learning.** McGill University 2003 [paper](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Survey-of-Exploration-Strategies-in-Reinforcement-McFarlane\u002F02761533d794ed9ed5dfd0295f2577e1e98c4fe2?p2df) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMcFarlane2003A.md)\n\n    *R. McFarlane*\n\n7. **A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress.** Artif. Intell. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06877) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FArora2021A.md)\n\n    *Saurabh Arora, Prashant Doshi*\n\n8. **A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.10619) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FPadakandla2022A.md)\n\n    *Sindhu Padakandla*\n\n9. **A Survey of Reinforcement Learning Informed by Natural Language.** IJCAI 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.03926) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLuketina2019A.md)\n\n    *Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel*\n\n10. **A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.06921) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMondal2020A.md)\n\n    *Amit Kumar Mondal*\n\n11. **A Survey of Zero-shot Generalisation in Deep Reinforcement Learning.** J. Artif. Intell. Res. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.09794.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FKirk2023A.md)\n\n    *Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel*\n\n12. **A Survey on Deep Reinforcement Learning for Audio-Based Applications.** Artif. Intell. Rev. 2023 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.00240.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLatif2023A.md)\n\n    *Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria*\n\n13. **A Survey on Deep Reinforcement Learning for Data Processing and Analytics.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.04526.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FCai2021A.md)\n\n    *Qingpeng Cai, Can Cui, Yiyuan Xiong, Wei Wang, Zhongle Xie, Meihui Zhang*\n\n14. **A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.06665.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FQing2022A.md)\n\n    *Yunpeng Qing, Shunyu Liu, Jie Song, Mingli Song*\n\n15. **A survey on intrinsic motivation in reinforcement learning.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.06976) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FAubret2019A.md)\n\n    *Arthur Aubret, Laëtitia Matignon, Salima Hassas*\n\n16. **A Survey on Reinforcement Learning for Combinatorial Optimization.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2008.12248.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FYang2020A.md)\n\n    *Yunhao Yang, Andrew B. Whinston*\n\n17. **A Survey on Reinforcement Learning for Recommender Systems.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.10665.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLin2021A.md)\n\n    *Yuanguo Lin, Yong Liu, Fan Lin, Pengcheng Wu, Wenhua Zeng, Chunyan Miao*\n\n18. **A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots.** CoRL 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.03772) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLynnerup2019A.md)\n\n    *Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam*\n\n19. **A Survey on Transformers in Reinforcement Learning.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.03044.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLi2023A.md)\n\n    *Wenzhe Li, Hao Luo, Zichuan Lin, Chongjie Zhang, Zongqing Lu, Deheng Ye*\n\n20. **Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.07865.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLinke2019Adapting.md)\n\n    *Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White*\n\n21. **Automated Reinforcement Learning (AutoRL): A Survey and Open Problems.** J. Artif. Intell. Res. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.03916) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FParker-Holder2022Automated.md)\n\n    *Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer*\n\n22. **Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics.** arXiv 2020 [paper](https:\u002F\u002Fwww.mdpi.com\u002F2227-7390\u002F8\u002F10\u002F1640) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMosavi2020Comprehensive.md)\n\n    *Amir Mosavi, Pedram Ghamisi, Yaser Faghan, Puhong Duan*\n\n23. **Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey.** J. Mach. Learn. Res. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.04960.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FNarvekar2020Curriculum.md)\n\n    *Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone*\n\n24. **Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.05598) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FPlaat2020Deep.md)\n\n    *Aske Plaat, Walter Kosters, Mike Preuss*\n\n25. **Deep Reinforcement Learning for Autonomous Driving: A Survey.** IEEE Trans. Intell. Transp. Syst. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.00444.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FKiran2022Deep.md)\n\n    *B. Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A. Al Sallab, Senthil Kumar Yogamani, Patrick Pérez*\n\n26. **Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.09475.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLiu2019Deep.md)\n\n    *Siqi Liu, Kee Yuan Ngiam, Mengling Feng*\n\n27. **Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.04462.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FZong2021Deep.md)\n\n    *Zefang Zong, Tao Feng, Tong Xia, Depeng Jin, Yong Li*\n\n28. **Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey.** IEEE Trans. Intell. Transp. Syst. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.00935.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FHaydari2022Deep.md)\n\n    *Ammar Haydari, Yasin Yilmaz*\n\n29. **Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.00123.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FPricope2021Deep.md)\n\n    *Tidor-Vlad Pricope*\n\n30. **Deep Reinforcement Learning: A Brief Survey.** IEEE Signal Process. Mag. 2017 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8103164) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FArulkumaran2017Deep.md)\n\n    *Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath*\n\n31. **Deep Reinforcement Learning: An Overview.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.07274) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLi2017Deep.md)\n\n    *Yuxi Li*\n\n32. **Derivative-Free Reinforcement Learning: A Review.** Frontiers Comput. Sci. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.05710) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FQian2021Derivative-Free.md)\n\n    *Hong Qian, Yang Yu*\n\n33. **Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.09003.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FDazeley2021Explainable.md)\n\n    *Richard Dazeley, Peter Vamplew, Francisco Cruz*\n\n34. **Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations.** IEEE CAA J. Autom. Sinica 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04577) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FBertsekas2019Feature-Based.md)\n\n    *Dimitri P. Bertsekas*\n\n35. **Model-based Reinforcement Learning: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.16712) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMoerland2020Model-based.md)\n\n    *Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker*\n\n36. **Reinforcement Learning for Combinatorial Optimization: A Survey.** Comput. Oper. Res. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.03600) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMazyavkina2021Reinforcement.md)\n\n    *Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev*\n\n37. **Reinforcement Learning in Healthcare: A Survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.08796.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FYu2019Reinforcement.md)\n\n    *Chao Yu, Jiming Liu, Shamim Nemati*\n\n38. **Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey.** SSCI 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.13303.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FZhao2020Sim-to-Real.md)\n\n    *Wenshuai Zhao, Jorge Peña Queralta, Tomi Westerlund*\n\n39. **Survey on reinforcement learning for language processing.** Artif. Intell. Rev. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.05565) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FUc-Cetina2023Survey.md)\n\n    *Víctor Uc-Cetina, Nicolás Navarro-Guerrero, Anabel Martín-González, Cornelius Weber, Stefan Wermter*\n\n40. **Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning.** SSCI 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.09381.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FSun2019Tutorial.md)\n\n    *Xudong Sun, Bernd Bischl*\n\n#### [Diffusion Models](#content)\n\n1. **A Survey on Generative Diffusion Model.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.02646) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FCao2022A.md)\n\n    *Hanqun Cao, Cheng Tan, Zhangyang Gao, Guangyong Chen, Pheng-Ann Heng, Stan Z. Li*\n\n2. **Diffusion Models for Medical Image Analysis: A Comprehensive Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.07804) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FKazerouni2022Diffusion.md)\n\n    *Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Mohsen Fayyaz, Ilker Hacihaliloglu, Dorit Merhof*\n\n3. **Diffusion Models in NLP: A Survey.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14671) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FZhu2023Diffusion.md)\n\n    **\n\n4. **Diffusion Models in Vision: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.04747.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FCroitoru2022Diffusion.md)\n\n    *Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Mubarak Shah*\n\n5. **Diffusion Models: A Comprehensive Survey of Methods and Applications.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.00796.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FYang2022Diffusion.md)\n\n    *Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, Bin Cui*\n\n6. **Efficient Diffusion Models for Vision: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.09292.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FUlhaq2022Efficient.md)\n\n    *Anwaar Ulhaq, Naveed Akhtar, Ganna Pogrebna*\n\n#### [Federated Learning](#content)\n\n1. **A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection.** arXiv 2019 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.09693.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FLi2019A.md)\n\n    *Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Xu Liu, Bingsheng He*\n\n2. **A Survey on Heterogeneous Federated Learning.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.04505.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FGao2022A.md)\n\n    *Dashan Gao, Xin Yao, Qiang Yang*\n\n3. **Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions.** Eng. Appl. Artif. Intell. 2021 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.06810.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FBlanco-Justicia2021Achieving.md)\n\n    *Alberto Blanco-Justicia, Josep Domingo-Ferrer, Sergio Martínez, David Sánchez, Adrian Flanagan, Kuan Eeik Tan*\n\n4. **Advances and Open Problems in Federated Learning.** Found. Trends Mach. Learn. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.04977) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FKairouz2021Advances.md)\n\n    *Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao*\n\n5. **Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications.** SIGKDD Explor. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.11812) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FFu2022Federated.md)\n\n    *Xingbo Fu, Binchi Zhang, Yushun Dong, Chen Chen, Jundong Li*\n\n6. **Federated Learning Challenges and Opportunities: An Outlook.** ICASSP 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.00807.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FDing2022Federated.md)\n\n    *Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang*\n\n7. **Fusion of Federated Learning and Industrial Internet of Things: A Survey.** arXiv 2021 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.00798.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FM.2021Fusion.md)\n\n    *Parimala M., R. M. Swarna Priya, Quoc-Viet Pham, Kapal Dev, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu, Thien Huynh-The*\n\n8. **Privacy and Robustness in Federated Learning: Attacks and Defenses.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06337) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FLyu2020Privacy.md)\n\n    *Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu*\n\n9. **Threats to Federated Learning: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.02133) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FLyu2020Threats.md)\n\n    *Lingjuan Lyu, Han Yu, Qiang Yang*\n\n10. **Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11545) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FJin2020Towards.md)\n\n    *Yilun Jin, Xiguang Wei, Yang Liu, Qiang Yang*\n\n#### [Few-Shot and Zero-Shot Learning](#content)\n\n1. **A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.06743.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FFew-Shot-and-Zero-Shot-Learning\u002FSong2022A.md)\n\n    *Yisheng Song, Ting Wang, Subrota K. Mondal, Jyoti Prakash Sahoo*\n\n2. **A Survey of Zero-shot Generalisation in Deep Reinforcement Learning.** J. Artif. Intell. Res. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.09794.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FFew-Shot-and-Zero-Shot-Learning\u002FKirk2023A.md)\n\n    *Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel*\n\n3. **A Survey of Zero-Shot Learning: Settings, Methods, and Applications.** ACM Trans. Intell. Syst. Technol. 2019 [paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3293318) [bib](\u002Fbib\u002FMachine-Learning\u002FFew-Shot-and-Zero-Shot-Learning\u002FWang2019A.md)\n\n    *Wei Wang, Vincent W. Zheng, Han Yu, Chunyan Miao*\n\n4. **A Survey on Few-Shot Class-Incremental Learning.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.08130) [bib](\u002Fbib\u002FMachine-Learning\u002FFew-Shot-and-Zero-Shot-Learning\u002FTian2023A.md)\n\n    *Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari*\n\n5. **A Survey on Machine Learning from Few Samples.** Pattern Recognition 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.02653) [bib](\u002Fbib\u002FMachine-Learning\u002FFew-Shot-and-Zero-Shot-Learning\u002FLu2020A.md)\n\n    *Jiang Lu, Pinghua Gong, Jieping Ye, Jianwei Zhang, Changshui Zhang*\n\n6. **Generalizing from a Few Examples: A Survey on Few-Shot Learning.** ACM Comput. Surv. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.05046) [bib](\u002Fbib\u002FMachine-Learning\u002FFew-Shot-and-Zero-Shot-Learning\u002FWang2021Generalizing.md)\n\n    *Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni*\n\n7. **Learning from Few Samples: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.15484) [bib](\u002Fbib\u002FMachine-Learning\u002FFew-Shot-and-Zero-Shot-Learning\u002FBendre2020Learning.md)\n\n    *Nihar Bendre, Hugo Terashima-Marín, Peyman Najafirad*\n\n#### [General Machine Learning](#content)\n\n1. **A Comprehensive Survey on Outlying Aspect Mining Methods.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.02637.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FSamariya2020A.md)\n\n    *Durgesh Samariya, Jiangang Ma, Sunil Aryal*\n\n2. **A survey and taxonomy of loss functions in machine learning.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.05579.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FCiampiconi2023A.md)\n\n    *Lorenzo Ciampiconi, Adam Elwood, Marco Leonardi, Ashraf Mohamed, Alessandro Rozza*\n\n3. **A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications.** Neural Networks 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.11437) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FSilva2019A.md)\n\n    *Leonardo Enzo Brito da Silva, Islam Elnabarawy, Donald C. Wunsch II*\n\n4. **A survey of dimensionality reduction techniques.** arXiv 2014 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1403.2877) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FSorzano2014A.md)\n\n    *Carlos Oscar Sánchez Sorzano, Javier Vargas, Alberto Domingo Pascual-Montano*\n\n5. **A Survey of Human-in-the-loop for Machine Learning.** Future Gener. Comput. Syst. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.00941) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FWu2022A.md)\n\n    *Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, Liang He*\n\n6. **A Survey of Learning Causality with Data: Problems and Methods.** ACM Comput. Surv. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.09337.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FGuo2021A.md)\n\n    *Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu*\n\n7. **A Survey of Learning on Small Data.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.14443.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FCao2022A.md)\n\n    *Xiaofeng Cao, Weixin Bu, Shengjun Huang, Ying-Peng Tang, Yaming Guo, Yi Chang, Ivor W. Tsang*\n\n8. **A Survey of Predictive Modelling under Imbalanced Distributions.** arXiv 2015 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1505.01658) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FBranco2015A.md)\n\n    *Paula Branco, Luís Torgo, Rita P. Ribeiro*\n\n9. **A Survey On (Stochastic Fractal Search) Algorithm.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01503) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FElKomy2021A.md)\n\n    *Mohammed ElKomy*\n\n10. **A Survey on Data Collection for Machine Learning: a Big Data - AI Integration Perspective.** IEEE Trans. Knowl. Data Eng. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.03402) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FRoh2021A.md)\n\n    *Yuji Roh, Geon Heo, Steven Euijong Whang*\n\n11. **A Survey on Distributed Machine Learning.** ACM Comput. Surv. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.09789) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FVerbraeken2021A.md)\n\n    *Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg, Tim Verbelen, Jan S. Rellermeyer*\n\n12. **A survey on feature weighting based K-Means algorithms.** J. Classif. 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.03483) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FAmorim2016A.md)\n\n    *Renato Cordeiro de Amorim*\n\n13. **A survey on graph kernels.** Appl. Netw. Sci. 2020 [paper](https:\u002F\u002Fappliednetsci.springeropen.com\u002Farticles\u002F10.1007\u002Fs41109-019-0195-3) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FKriege2020A.md)\n\n    *Nils M. Kriege, Fredrik D. Johansson, Christopher Morris*\n\n14. **A Survey on Large-scale Machine Learning.** IEEE Trans. Knowl. Data Eng. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.03911) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FWang2022A.md)\n\n    *Meng Wang, Weijie Fu, Xiangnan He, Shijie Hao, Xindong Wu*\n\n15. **A Survey on Optimal Transport for Machine Learning: Theory and Applications.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.01963) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FTorres2021A.md)\n\n    *Luis Caicedo Torres, Luiz Manella Pereira, M. Hadi Amini*\n\n16. **A Survey on Resilient Machine Learning.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.03184) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FKumar2017A.md)\n\n    *Atul Kumar, Sameep Mehta*\n\n17. **A Survey on Surrogate Approaches to Non-negative Matrix Factorization.** arXiv 2018 [paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10013-018-0315-x.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FFernsel2018A.md)\n\n    *Pascal Fernsel, Peter Maass*\n\n18. **Adversarial Examples in Modern Machine Learning: A Review.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.05268) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FWiyatno2019Adversarial.md)\n\n    *Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, Archy de Berker*\n\n19. **Algorithms Inspired by Nature: A Survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.01893) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FGupta2019Algorithms.md)\n\n    *Pranshu Gupta*\n\n20. **An Overview of Privacy in Machine Learning.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.08679.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FCristofaro2020An.md)\n\n    *Emiliano De Cristofaro*\n\n21. **Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.09316) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FWang2021Are.md)\n\n    *Zhenhua Wang, Olanrewaju Akande, Jason Poulos, Fan Li*\n\n22. **Certification of embedded systems based on Machine Learning: A survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.07221) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FVidot2021Certification.md)\n\n    *Guillaume Vidot, Christophe Gabreau, Ileana Ober, Iulian Ober*\n\n23. **Class-incremental learning: survey and performance evaluation.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.15277.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FMasana2020Class-incremental.md)\n\n    *Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, Joost van de Weijer*\n\n24. **Data and its (dis)contents: A survey of dataset development and use in machine learning research.** Patterns 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.05345) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FPaullada2021Data.md)\n\n    *Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender, Emily Denton, Alex Hanna*\n\n25. **Generating Artificial Outliers in the Absence of Genuine Ones - a Survey.** ACM Trans. Knowl. Discov. Data 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.03646) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FSteinbuss2021Generating.md)\n\n    *Georg Steinbuss, Klemens Böhm*\n\n26. **Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: A Survey of Approximation and Consistency Results.** UAI 1998 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1301.7390) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FJiang1998Hierarchical.md)\n\n    *Wenxin Jiang, Martin A. Tanner*\n\n27. **Hyperbox-based machine learning algorithms: A comprehensive survey.** Soft Comput. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.11303) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FKhuat2021Hyperbox-based.md)\n\n    *Thanh Tung Khuat, Dymitr Ruta, Bogdan Gabrys*\n\n28. **Introduction to Core-sets: an Updated Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.09384.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FFeldman2020Introduction.md)\n\n    *Dan Feldman*\n\n29. **Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.02154) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FGhojogh2021Laplacian-Based.md)\n\n    *Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley*\n\n30. **Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities.** VLSI-SoC 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.01915) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FSisejkovic2021Logic.md)\n\n    *Dominik Sisejkovic, Lennart M. Reimann, Elmira Moussavi, Farhad Merchant, Rainer Leupers*\n\n31. **Machine Learning at the Network Edge: A Survey.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.00080) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FMurshed2022Machine.md)\n\n    *M. G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, Faraz Hussain*\n\n32. **Machine Learning for Spatiotemporal Sequence Forecasting: A Survey.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.06865) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FShi2018Machine.md)\n\n    *Xingjian Shi, Dit-Yan Yeung*\n\n33. **Machine Learning in Network Centrality Measures: Tutorial and Outlook.** ACM Comput. Surv. 2019 [paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3237192) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FGrando2019Machine.md)\n\n    *Felipe Grando, Lisandro Zambenedetti Granville, Luís C. Lamb*\n\n34. **Machine Learning Testing: Survey, Landscapes and Horizons.** IEEE Trans. Software Eng. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.10742) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FZhang2022Machine.md)\n\n    *Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu*\n\n35. **Machine Learning that Matters.** ICML 2012 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1206.4656) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FWagstaff2012Machine.md)\n\n    *Kiri Wagstaff*\n\n36. **Machine Learning with World Knowledge: The Position and Survey.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.02908) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FSong2017Machine.md)\n\n    *Yangqiu Song, Dan Roth*\n\n37. **Mean-Field Learning: a Survey.** arXiv 2012 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1210.4657) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FTembine2012Mean-Field.md)\n\n    *Hamidou Tembine, Raúl Tempone, Pedro Vilanova*\n\n38. **Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08136) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FGhojogh2020Multidimensional.md)\n\n    *Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley*\n\n39. **Multimodal Machine Learning: A Survey and Taxonomy.** IEEE Trans. Pattern Anal. Mach. Intell. 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09406) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FBaltrusaitis2019Multimodal.md)\n\n    *Tadas Baltrusaitis, Chaitanya Ahuja, Louis-Philippe Morency*\n\n40. **Multi-objective multi-agent decision making: a utility-based analysis and survey.** AAMAS 2020 [paper](https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10458-019-09433-x.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FRadulescu2020Multi-objective.md)\n\n    *Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé*\n\n41. **Rank-based Decomposable Losses in Machine Learning: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.08768.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FHu2022Rank-based.md)\n\n    *Shu Hu, Xin Wang, Siwei Lyu*\n\n42. **Rational Kernels: A survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.13800.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FGhose2019Rational.md)\n\n    *Abhishek Ghose*\n\n43. **Sampling Constrained Continuous Probability Distributions: A Review.** WIREs Computational Statistics 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.12403.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FLan2022Sampling.md)\n\n    *Shiwei Lan, Lulu Kang*\n\n44. **Statistical Queries and Statistical Algorithms: Foundations and Applications.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.00557) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FReyzin2020Statistical.md)\n\n    *Lev Reyzin*\n\n45. **Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey.** arXiv 2011 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1111.6925) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FZhou2011Structure.md)\n\n    *Yang Zhou*\n\n46. **Survey & Experiment: Towards the Learning Accuracy.** arXiv 2010 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1012.4051) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FZhu2010Survey.md)\n\n    *Zeyuan Allen Zhu*\n\n47. **Survey on Feature Selection.** arXiv 2015 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1510.02892) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FAbdallah2015Survey.md)\n\n    *Tarek Amr Abdallah, Beatriz de la Iglesia*\n\n48. **Survey on Multi-output Learning.** IEEE Trans. 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Tsang, Yew-Soon Ong, Chen Gong, Xiaobo Shen*\n\n49. **Survey: Machine Learning in Production Rendering.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.12518) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FZhu2020Survey.md)\n\n    *Shilin Zhu*\n\n50. **The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses.** Theory of Evolutionary Computation 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.10087) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FSudholt2020The.md)\n\n    *Dirk Sudholt*\n\n51. **The Mathematics of Artificial Intelligence.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.08890.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FKutyniok2022The.md)\n\n    *Gitta Kutyniok*\n\n52. **Towards Causal Representation Learning.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.11107) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FSchölkopf2021Towards.md)\n\n    *Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio*\n\n53. **Towards Out-Of-Distribution Generalization: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.13624.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FShen2021Towards.md)\n\n    *Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui*\n\n54. **Verification for Machine Learning, Autonomy, and Neural Networks Survey.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.01989) [bib](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FXiang2018Verification.md)\n\n    *Weiming Xiang, Patrick Musau, Ayana A. 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Surv. 2019 [paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3236009) [bib](\u002Fbib\u002FMachine-Learning\u002FInterpretability-and-Analysis\u002FGuidotti2019A.md)\n\n    *Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, Dino Pedreschi*\n\n4. **A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI.** IEEE Trans. Neural Networks Learn. 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Huber*\n\n8. **A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.01792) [bib](\u002Fbib\u002FMachine-Learning\u002FInterpretability-and-Analysis\u002FShahroudnejad2021A.md)\n\n    *Atefeh Shahroudnejad*\n\n9. **Benchmarking and Survey of Explanation Methods for Black Box Models.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.13076.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FInterpretability-and-Analysis\u002FBodria2021Benchmarking.md)\n\n    *Francesco Bodria, Fosca Giannotti, Riccardo Guidotti, Francesca Naretto, Dino Pedreschi, Salvatore Rinzivillo*\n\n10. **Causal Interpretability for Machine Learning - Problems, Methods and Evaluation.** SIGKDD Explor. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.03934) [bib](\u002Fbib\u002FMachine-Learning\u002FInterpretability-and-Analysis\u002FMoraffah2020Causal.md)\n\n    *Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu*\n\n11. **Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.** Inf. 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On the Interpretability of Machine Learning for Medical Applications: a survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00353) [bib](\u002Fbib\u002FMachine-Learning\u002FInterpretability-and-Analysis\u002FBanegas-Luna2020When.md)\n\n    *Antonio-Jesús Banegas-Luna, Jorge Peña-García, Adrian Iftene, Fiorella Guadagni, Patrizia Ferroni, Noemi Scarpato, Fabio Massimo Zanzotto, Andrés Bueno-Crespo, Horacio Pérez Sánchez*\n\n32. **XAI Methods for Neural Time Series Classification: A Brief Review.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.08009.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FInterpretability-and-Analysis\u002FSimic2021XAI.md)\n\n    *Ilija Simic, Vedran Sabol, Eduardo E. Veas*\n\n#### [Knowledge Distillation](#content)\n\n1. **A Selective Survey on Versatile Knowledge Distillation Paradigm for Neural Network Models.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.14554.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FKnowledge-Distillation\u002FKu2020A.md)\n\n    *Jeong-Hoe Ku, Jihun Oh, Young-Yoon Lee, Gaurav Pooniwala, SangJeong Lee*\n\n2. **Distilling the Knowledge in a Neural Network.** arXiv 2015 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1503.02531.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FKnowledge-Distillation\u002FHinton2015Distilling.md)\n\n    *Geoffrey E. Hinton, Oriol Vinyals, Jeffrey Dean*\n\n3. **Knowledge Distillation: A Survey.** Int. J. Comput. Vis. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.05525) [bib](\u002Fbib\u002FMachine-Learning\u002FKnowledge-Distillation\u002FGou2021Knowledge.md)\n\n    *Jianping Gou, Baosheng Yu, Stephen J. Maybank, Dacheng Tao*\n\n#### [Meta Learning](#content)\n\n1. **A Comprehensive Overview and Survey of Recent Advances in Meta-Learning.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.11149) [bib](\u002Fbib\u002FMachine-Learning\u002FMeta-Learning\u002FPeng2020A.md)\n\n    *Huimin Peng*\n\n2. **A Survey of Deep Meta-Learning.** Artif. Intell. Rev. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.03522.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FMeta-Learning\u002FHuisman2021A.md)\n\n    *Mike Huisman, Jan N. van Rijn, Aske Plaat*\n\n3. **A Survey of Meta-Reinforcement Learning.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.08028.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FMeta-Learning\u002FBeck2023A.md)\n\n    *Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa M. Zintgraf, Chelsea Finn, Shimon Whiteson*\n\n4. **Meta-learning for Few-shot Natural Language Processing: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.09604) [bib](\u002Fbib\u002FMachine-Learning\u002FMeta-Learning\u002FYin2020Meta-learning.md)\n\n    *Wenpeng Yin*\n\n5. **Meta-Learning in Neural Networks: A Survey.** IEEE Trans. Pattern Anal. Mach. Intell. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.05439) [bib](\u002Fbib\u002FMachine-Learning\u002FMeta-Learning\u002FHospedales2022Meta-Learning.md)\n\n    *Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey*\n\n6. **Meta-Learning: A Survey.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.03548) [bib](\u002Fbib\u002FMachine-Learning\u002FMeta-Learning\u002FVanschoren2018Meta-Learning.md)\n\n    *Joaquin Vanschoren*\n\n#### [Metric Learning](#content)\n\n1. **A Survey on Metric Learning for Feature Vectors and Structured Data.** arXiv 2013 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1306.6709) [bib](\u002Fbib\u002FMachine-Learning\u002FMetric-Learning\u002FBellet2013A.md)\n\n    *Aurélien Bellet, Amaury Habrard, Marc Sebban*\n\n2. **A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges.** Neurocomputing 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.05944) [bib](\u002Fbib\u002FMachine-Learning\u002FMetric-Learning\u002FSuárez2021A.md)\n\n    *Juan-Luis Suárez, Salvador García, Francisco Herrera*\n\n#### [ML and DL Applications](#content)\n\n1. **A Comprehensive Survey on Community Detection with Deep Learning.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.12584) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FSu2021A.md)\n\n    *Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cécile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu*\n\n2. **A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.06801.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FJi2020A.md)\n\n    *Shulei Ji, Jing Luo, Xinyu Yang*\n\n3. **A Comprehensive Survey on Graph Anomaly Detection with Deep Learning.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.07178.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FMa2021A.md)\n\n    *Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Quan Z. Sheng, Hui Xiong*\n\n4. **A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.02629.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FYousefi2019A.md)\n\n    *Niloofar Yousefi, Marie Alaghband, Ivan Garibay*\n\n5. **A guide to deep learning in healthcare.** Nature Medicine 2019 [paper](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-018-0316-z) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FEsteva2019A.md)\n\n    *Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, Jeff Dean*\n\n6. **A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning.** IEEE Trans. Knowl. Data Eng. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.01669.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FJin2023A.md)\n\n    *Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang*\n\n7. **A Survey of Deep Learning Applications to Autonomous Vehicle Control.** IEEE Trans. Intell. Transp. Syst. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.10773.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FKuutti2021A.md)\n\n    *Sampo Kuutti, Richard Bowden, Yaochu Jin, Phil Barber, Saber Fallah*\n\n8. **A Survey of Deep Learning Techniques for Autonomous Driving.** J. Field Robotics 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.07738.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FGrigorescu2020A.md)\n\n    *Sorin Mihai Grigorescu, Bogdan Trasnea, Tiberiu T. 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Bowyer*\n\n38. **Graph Representation Learning in Biomedicine.** arXiv 2021 [paper](http:\u002F\u002Farxiv.org\u002Fabs\u002F2104.04883) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FLi2021Graph.md)\n\n    *Michelle M. Li, Kexin Huang, Marinka Zitnik*\n\n39. **Graph-based Deep Learning for Communication Networks: A Survey.** Comput. Commun. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.02533) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FJiang2022Graph-based.md)\n\n    *Weiwei Jiang*\n\n40. **How Developers Iterate on Machine Learning Workflows - A Survey of the Applied Machine Learning Literature.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10311) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FXin2018How.md)\n\n    *Doris Xin, Litian Ma, Shuchen Song, Aditya G. Parameswaran*\n\n41. **Human Action Recognition from Various Data Modalities: A Review.** IEEE Trans. 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Anand Kumar, Jeny Rajan*\n\n49. **MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.14500.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FDu2022MolGenSurvey.md)\n\n    *Yuanqi Du, Tianfan Fu, Jimeng Sun, Shengchao Liu*\n\n50. **Multi-modal Sensor Fusion for Auto Driving Perception: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.02703.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FHuang2022Multi-modal.md)\n\n    *Keli Huang, Botian Shi, Xiang Li, Xin Li, Siyuan Huang, Yikang Li*\n\n51. **Physics-Guided Deep Learning for Dynamical Systems: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.01272) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FWang2021Physics-Guided.md)\n\n    *Rui Wang*\n\n52. **Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.08064.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FHao2022Physics-Informed.md)\n\n    *Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu*\n\n53. **Predicting the Future from First Person (Egocentric) Vision: A Survey.** Comput. Vis. Image Underst. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.13411.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FRodin2021Predicting.md)\n\n    *Ivan Rodin, Antonino Furnari, Dimitrios Mavroeidis, Giovanni Maria Farinella*\n\n54. **Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.12707.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FTadesse2020Prediction.md)\n\n    *Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, Komminist Weldemariam*\n\n55. **Requirement Engineering Challenges for AI-intense Systems Development.** WAIN@ICSE 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.10270.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FHeyn2021Requirement.md)\n\n    *Hans-Martin Heyn, Eric Knauss, Amna Pir Muhammad, Olof Eriksson, Jennifer Linder, Padmini Subbiah, Shameer Kumar Pradhan, Sagar Tungal*\n\n56. **Short-term Traffic Prediction with Deep Neural Networks: A Survey.** IEEE Access 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.00712) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FLee2021Short-term.md)\n\n    *Kyungeun Lee, Moonjung Eo, Euna Jung, Yoonjin Yoon, Wonjong Rhee*\n\n57. **Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.06646.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FZohrevand2019Should.md)\n\n    *Zahra Zohrevand, Uwe Glässer*\n\n58. **The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.02621.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FIbitoye2019The.md)\n\n    *Olakunle Ibitoye, Rana Abou Khamis, Ashraf Matrawy, M. Omair Shafiq*\n\n59. **Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey.** IEEE Access 2018 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8294186) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FAkhtar2018Threat.md)\n\n    *Naveed Akhtar, Ajmal S. Mian*\n\n60. **Towards Controllable Protein Design with Conditional Transformers.** Nat. Mach. Intell. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fftp\u002Farxiv\u002Fpapers\u002F2201\u002F2201.07338.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FFerruz2022Towards.md)\n\n    *Noelia Ferruz, Birte Höcker*\n\n61. **Transformers in Remote Sensing: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.01206.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FAleissaee2022Transformers.md)\n\n    *Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal, Gui-Song Xia, Fahad Shahbaz Khan*\n\n62. **Transformers in Time Series: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.07125.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FWen2022Transformers.md)\n\n    *Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun*\n\n63. **Understanding racial bias in health using the Medical Expenditure Panel Survey data.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.01509.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FSingh2019Understanding.md)\n\n    *Moninder Singh, Karthikeyan Natesan Ramamurthy*\n\n64. **Urban flows prediction from spatial-temporal data using machine learning: A survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.10218) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FXie2019Urban.md)\n\n    *Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang*\n\n65. **Using Deep Learning for Visual Decoding and Reconstruction from Brain Activity: A Review.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.04169.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FHorn2021Using.md)\n\n    *Madison Van Horn*\n\n66. **Utilising Graph Machine Learning within Drug Discovery and Development.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.05716.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FML-and-DL-Applications\u002FGaudelet2020Utilising.md)\n\n    *Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell, Michael M. Bronstein, Jake P. Taylor-King*\n\n#### [Model Compression and Acceleration](#content)\n\n1. **A Survey of Model Compression and Acceleration for Deep Neural Networks.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09282) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FCheng2017A.md)\n\n    *Yu Cheng, Duo Wang, Pan Zhou, Tao Zhang*\n\n2. **A Survey of Quantization Methods for Efficient Neural Network Inference.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.13630) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FGholami2021A.md)\n\n    *Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer*\n\n3. **A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.03954.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FMishra2020A.md)\n\n    *Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta*\n\n4. **A Survey on GAN Acceleration Using Memory Compression Technique.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.06626.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FTantawy2021A.md)\n\n    *Dina Tantawy, Mohamed Zahran, Amr Wassal*\n\n5. **A Survey on Methods and Theories of Quantized Neural Networks.** arXiv 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.04752) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FGuo2018A.md)\n\n    *Yunhui Guo*\n\n6. **A Survey on Model Compression and Acceleration for Pretrained Language Models.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.07105) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FXu2022A.md)\n\n    *Canwen Xu, Julian J. McAuley*\n\n7. **An Overview of Neural Network Compression.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.03669) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FO'Neill2020An.md)\n\n    *James O'Neill*\n\n8. **Compression of Deep Learning Models for Text: A Survey.** ACM Trans. Knowl. Discov. Data 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.05221) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FGupta2022Compression.md)\n\n    *Manish Gupta, Puneet Agrawal*\n\n9. **Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.08099.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FWimmer2022Dimensionality.md)\n\n    *Paul Wimmer, Jens Mehnert, Alexandru Paul Condurache*\n\n10. **Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.08962.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FMenghani2021Efficient.md)\n\n    *Gaurav Menghani*\n\n11. **Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.04275) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FLiu2020Pruning.md)\n\n    *Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah*\n\n12. **Pruning and Quantization for Deep Neural Network Acceleration: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.09671) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FLiang2021Pruning.md)\n\n    *Tailin Liang, John Glossner, Lei Wang, Shaobo Shi*\n\n13. **Survey of Machine Learning Accelerators.** HPEC 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.00993) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FReuther2020Survey.md)\n\n    *Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner*\n\n14. **Survey on Large Scale Neural Network Training.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.10435) [bib](\u002Fbib\u002FMachine-Learning\u002FModel-Compression-and-Acceleration\u002FGusak2022Survey.md)\n\n    *Julia Gusak, Daria Cherniuk, Alena Shilova, Alexandr Katrutsa, Daniel Bershatsky, Xunyi Zhao, Lionel Eyraud-Dubois, Oleg Shlyazhko, Denis Dimitrov, Ivan V. Oseledets, Olivier Beaumont*\n\n#### [Multi-Label Learning](#content)\n\n1. **A Review on Multi-Label Learning Algorithms.** IEEE Trans. Knowl. Data Eng. 2014 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F6471714) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Label-Learning\u002FZhang2014A.md)\n\n    *Min-Ling Zhang, Zhi-Hua Zhou*\n\n2. **Multi-Label Classification: An Overview.** Int. J. Data Warehous. Min. 2007 [paper](https:\u002F\u002Fwww.igi-global.com\u002Farticle\u002Fmulti-label-classification\u002F1786) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Label-Learning\u002FTsoumakas2007Multi-Label.md)\n\n    *Grigorios Tsoumakas, Ioannis Katakis*\n\n3. **Multi-label learning: a review of the state of the art and ongoing research.** WIREs Data Mining Knowl. Discov. 2014 [paper](https:\u002F\u002Fwires.onlinelibrary.wiley.com\u002Fdoi\u002Fabs\u002F10.1002\u002Fwidm.1139) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Label-Learning\u002FGalindo2014Multi-label.md)\n\n    *Eva Lucrecia Gibaja Galindo, Sebastián Ventura*\n\n4. **The Emerging Trends of Multi-Label Learning.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.11197) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Label-Learning\u002FLiu2020The.md)\n\n    *Weiwei Liu, Xiaobo Shen, Haobo Wang, Ivor W. Tsang*\n\n#### [Multi-Task and Multi-View Learning](#content)\n\n1. **A brief review on multi-task learning.** Multim. Tools Appl. 2018 [paper](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11042-018-6463-x) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Task-and-Multi-View-Learning\u002FThung2018A.md)\n\n    *Kim-Han Thung, Chong-Yaw Wee*\n\n2. **A Survey on Multi-Task Learning.** IEEE Trans. Knowl. Data Eng. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08114) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Task-and-Multi-View-Learning\u002FZhang2022A.md)\n\n    *Yu Zhang, Qiang Yang*\n\n3. **A Survey on Multi-view Learning.** arXiv 2013 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1304.5634) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Task-and-Multi-View-Learning\u002FXu2013A.md)\n\n    *Chang Xu, Dacheng Tao, Chao Xu*\n\n4. **An overview of multi-task learning.** National Science Review 2017 [paper](https:\u002F\u002Facademic.oup.com\u002Fnsr\u002Farticle\u002F5\u002F1\u002F30\u002F4101432) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Task-and-Multi-View-Learning\u002FZhang2017An.md)\n\n    *Yu Zhang, Qiang Yang*\n\n5. **An Overview of Multi-Task Learning in Deep Neural Networks.** arXiv 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05098) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Task-and-Multi-View-Learning\u002FRuder2017An.md)\n\n    *Sebastian Ruder*\n\n6. **Multi-Task Learning for Dense Prediction Tasks: A Survey.** IEEE Trans. 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Lett. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.16008) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Task-and-Multi-View-Learning\u002FWorsham2020Multi-task.md)\n\n    *Joseph Worsham, Jugal Kalita*\n\n8. **Multi-Task Learning with Deep Neural Networks: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.09796) [bib](\u002Fbib\u002FMachine-Learning\u002FMulti-Task-and-Multi-View-Learning\u002FCrawshaw2020Multi-Task.md)\n\n    *Michael Crawshaw*\n\n#### [Online Learning](#content)\n\n1. **A Survey of Algorithms and Analysis for Adaptive Online Learning.** J. Mach. Learn. Res. 2017 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1403.3465) [bib](\u002Fbib\u002FMachine-Learning\u002FOnline-Learning\u002FMcMahan2017A.md)\n\n    *H. Brendan McMahan*\n\n2. **Online Continual Learning in Image Classification: An Empirical Survey.** Neurocomputing 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.10423) [bib](\u002Fbib\u002FMachine-Learning\u002FOnline-Learning\u002FMai2022Online.md)\n\n    *Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, Scott Sanner*\n\n3. **Online Learning: A Comprehensive Survey.** Neurocomputing 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.02871) [bib](\u002Fbib\u002FMachine-Learning\u002FOnline-Learning\u002FHoi2021Online.md)\n\n    *Steven C. H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao*\n\n4. **Preference-based Online Learning with Dueling Bandits: A Survey.** J. Mach. Learn. Res. 2021 [paper](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv22\u002F18-546.html) [bib](\u002Fbib\u002FMachine-Learning\u002FOnline-Learning\u002FBengs2021Preference-based.md)\n\n    *Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier*\n\n#### [Optimization](#content)\n\n1. **A Survey of Optimization Methods from a Machine Learning Perspective.** IEEE Trans. Cybern. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.06821) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FSun2020A.md)\n\n    *Shiliang Sun, Zehui Cao, Han Zhu, Jing Zhao*\n\n2. **A Systematic and Meta-analysis Survey of Whale Optimization Algorithm.** Comput. Intell. Neurosci. 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.08763) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FMohammed2019A.md)\n\n    *Hardi M. Mohammed, Shahla U. Umar, Tarik A. Rashid*\n\n3. **An overview of gradient descent optimization algorithms.** arXiv 2016 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.04747) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FRuder2016An.md)\n\n    *Sebastian Ruder*\n\n4. **Convex Optimization Overview.** CiteSeerX 2008 [paper](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fsummary?doi=10.1.1.142.6470) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FKolter2008Convex.md)\n\n    *Zico Kolter, Honglak Lee*\n\n5. **Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.02558) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FOsaba2021Evolutionary.md)\n\n    *Eneko Osaba, Aritz D. Martinez, Javier Del Ser*\n\n6. **Gradient Boosting Machine: A Survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.06951) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FHe2019Gradient.md)\n\n    *Zhiyuan He, Danchen Lin, Thomas Lau, Mike Wu*\n\n7. **Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond.** IEEE Trans. Pattern Anal. Mach. Intell. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.11517) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FLiu2022Investigating.md)\n\n    *Risheng Liu, Jiaxin Gao, Jin Zhang, Deyu Meng, Zhouchen Lin*\n\n8. **Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking.** IEEE Access 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.11081.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FVesselinova2020Learning.md)\n\n    *Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez, Magnus Boman*\n\n9. **Nature-Inspired Optimization Algorithms: Research Direction and Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.04013) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FSachan2021Nature-Inspired.md)\n\n    *Rohit Kumar Sachan, Dharmender Singh Kushwaha*\n\n10. **Optimization for deep learning: theory and algorithms.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.08957) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FSun2019Optimization.md)\n\n    *Ruoyu Sun*\n\n11. **Optimization Problems for Machine Learning: A Survey.** Eur. J. Oper. Res. 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.05331) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FGambella2021Optimization.md)\n\n    *Claudio Gambella, Bissan Ghaddar, Joe Naoum-Sawaya*\n\n12. **Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives.** Mach. Learn. Knowl. Extr. 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.05319) [bib](\u002Fbib\u002FMachine-Learning\u002FOptimization\u002FSengupta2019Particle.md)\n\n    *Saptarshi Sengupta, Sanchita Basak, Richard A. 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Jothi Prakash, L. M. Nithya*\n\n6. **Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.12694.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FSemi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u002FShao2021Deep.md)\n\n    *Feifei Shao, Long Chen, Jian Shao, Wei Ji, Shaoning Xiao, Lu Ye, Yueting Zhuang, Jun Xiao*\n\n7. **Graph-based Semi-supervised Learning: A Comprehensive Review.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.13303) [bib](\u002Fbib\u002FMachine-Learning\u002FSemi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u002FSong2021Graph-based.md)\n\n    *Zixing Song, Xiangli Yang, Zenglin Xu, Irwin King*\n\n8. **Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.09574) [bib](\u002Fbib\u002FMachine-Learning\u002FSemi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u002FMey2019Improvability.md)\n\n    *Alexander Mey, Marco Loog*\n\n9. **Learning from positive and unlabeled data: a survey.** Mach. Learn. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.04820) [bib](\u002Fbib\u002FMachine-Learning\u002FSemi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u002FBekker2020Learning.md)\n\n    *Jessa Bekker, Jesse Davis*\n\n10. **Robust Deep Semi-Supervised Learning: A Brief Introduction.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.05975.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FSemi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u002FGuo2022Robust.md)\n\n    *Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li*\n\n11. **Self-Supervised Learning for Recommender Systems: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.15876.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FSemi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u002FYu2022Self-Supervised.md)\n\n    *Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, Zi Huang*\n\n12. **Self-Supervised Learning for Videos: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.00419.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FSemi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u002FSchiappa2022Self-Supervised.md)\n\n    *Madeline C. Schiappa, Yogesh S. Rawat, Mubarak Shah*\n\n13. **Unsupervised Cross-Lingual Representation Learning.** ACL 2019 [paper](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-4007.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FSemi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u002FRuder2019Unsupervised.md)\n\n    *Sebastian Ruder, Anders Søgaard, Ivan Vulic*\n\n#### [Transfer Learning](#content)\n\n1. **A Comprehensive Survey on Transfer Learning.** Proc. IEEE 2021 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.02685) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FZhuang2021A.md)\n\n    *Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, Qing He*\n\n2. **A Survey of Unsupervised Deep Domain Adaptation.** ACM Trans. Intell. Syst. Technol. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.02849) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FWilson2020A.md)\n\n    *Garrett Wilson, Diane J. Cook*\n\n3. **A Survey on Deep Transfer Learning.** ICANN 2018 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.01974) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FTan2018A.md)\n\n    *Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, Chunfang Liu*\n\n4. **A survey on domain adaptation theory: learning bounds and theoretical guarantees.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.11829) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FRedko2020A.md)\n\n    *Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani*\n\n5. **A Survey on Negative Transfer.** IEEE\u002FCAA Journal of Automatica Sinica 2020 [paper](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.00909.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FZhang2020A.md)\n\n    *Wen Zhang, Lingfei Deng, Lei Zhang, Dongrui Wu*\n\n6. **A Survey on Transfer Learning.** IEEE Trans. Knowl. Data Eng. 2010 [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F5288526) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FPan2010A.md)\n\n    *Sinno Jialin Pan, Qiang Yang*\n\n7. **A Survey on Transfer Learning in Natural Language Processing.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.04239) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FAlyafeai2020A.md)\n\n    *Zaid Alyafeai, Maged Saeed AlShaibani, Irfan Ahmad*\n\n8. **Evolution of transfer learning in natural language processing.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.07370) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FMalte2019Evolution.md)\n\n    *Aditya Malte, Pratik Ratadiya*\n\n9. **Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.** J. Mach. Learn. Res. 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.10683.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FRaffel2020Exploring.md)\n\n    *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*\n\n10. **Neural Unsupervised Domain Adaptation in NLP---A Survey.** COLING 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.00632) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FRamponi2020Neural.md)\n\n    *Alan Ramponi, Barbara Plank*\n\n11. **Source-Free Unsupervised Domain Adaptation: A Survey.** arXiv 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.00265.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FFang2023Source-Free.md)\n\n    *Yuqi Fang, Pew-Thian Yap, Weili Lin, Hongtu Zhu, Mingxia Liu*\n\n12. **Transfer Adaptation Learning: A Decade Survey.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.04687.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FZhang2019Transfer.md)\n\n    *Lei Zhang*\n\n13. **Transfer Learning for Reinforcement Learning Domains: A Survey.** J. Mach. Learn. Res. 2009 [paper](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.5555\u002F1577069.1755839) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FTaylor2009Transfer.md)\n\n    *Matthew E. Taylor, Peter Stone*\n\n14. **Transfer Learning in Deep Reinforcement Learning: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07888) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FZhu2020Transfer.md)\n\n    *Zhuangdi Zhu, Kaixiang Lin, Jiayu Zhou*\n\n15. **Transferability in Deep Learning: A Survey.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.05867.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FJiang2022Transferability.md)\n\n    *Junguang Jiang, Yang Shu, Jianmin Wang, Mingsheng Long*\n\n#### [Trustworthy Machine Learning](#content)\n\n1. **A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.08570.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FDai2022A.md)\n\n    *Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, Suhang Wang*\n\n2. **A Survey of Neural Trojan Attacks and Defenses in Deep Learning.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.07183.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FWang2022A.md)\n\n    *Jie Wang, Ghulam Mubashar Hassan, Naveed Akhtar*\n\n3. **A Survey of Privacy Attacks in Machine Learning.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.07646) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FRigaki2020A.md)\n\n    *Maria Rigaki, Sebastian Garcia*\n\n4. **A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability.** Comput. Sci. Rev. 2020 [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1574013719302527?via%3Dihub) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FHuang2020A.md)\n\n    *Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, Xinping Yi*\n\n5. **A Survey on Bias and Fairness in Machine Learning.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.09635) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FMehrabi2022A.md)\n\n    *Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan*\n\n6. **Backdoor Learning: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.08745) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FLi2020Backdoor.md)\n\n    *Yiming Li, Baoyuan Wu, Yong Jiang, Zhifeng Li, Shu-Tao Xia*\n\n7. **Differential Privacy and Machine Learning: a Survey and Review.** arXiv 2014 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1412.7584) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FJi2014Differential.md)\n\n    *Zhanglong Ji, Zachary Chase Lipton, Charles Elkan*\n\n8. **Fairness in Machine Learning: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.04053.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FCaton2020Fairness.md)\n\n    *Simon Caton, Christian Haas*\n\n9. **Local Differential Privacy and Its Applications: A Comprehensive Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.03686) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FYang2020Local.md)\n\n    *Mengmeng Yang, Lingjuan Lyu, Jun Zhao, Tianqing Zhu, Kwok-Yan Lam*\n\n10. **Privacy in Deep Learning: A Survey.** arXiv 2020 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.12254) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FMireshghallah2020Privacy.md)\n\n    *Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh*\n\n11. **Taxonomy of Machine Learning Safety: A Survey and Primer.** ACM Comput. Surv. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.04823) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FMohseni2023Taxonomy.md)\n\n    *Sina Mohseni, Haotao Wang, Chaowei Xiao, Zhiding Yu, Zhangyang Wang, Jay Yadawa*\n\n12. **Technology Readiness Levels for Machine Learning Systems.** arXiv 2021 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.03989.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FLavin2021Technology.md)\n\n    *Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atilim Günes Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, James Parr*\n\n13. **The Creation and Detection of Deepfakes: A Survey.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.11138) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FMirsky2022The.md)\n\n    *Yisroel Mirsky, Wenke Lee*\n\n14. **Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.13243.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FRäuker2022Toward.md)\n\n    *Tilman Räuker, Anson Ho, Stephen Casper, Dylan Hadfield-Menell*\n\n15. **Trustworthy AI: From Principles to Practices.** ACM Comput. Surv. 2023 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.01167.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FLi2023Trustworthy.md)\n\n    *Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, Bowen Zhou*\n\n16. **Trustworthy Graph Neural Networks: Aspects, Methods and Trends.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.07424.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FZhang2022Trustworthy.md)\n\n    *He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei*\n\n17. **Tutorial: Safe and Reliable Machine Learning.** arXiv 2019 [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.07204) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FSaria2019Tutorial.md)\n\n    *Suchi Saria, Adarsh Subbaswamy*\n\n18. **When Machine Learning Meets Privacy: A Survey and Outlook.** ACM Comput. Surv. 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.11819.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FLiu2022When.md)\n\n    *Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, Zihuai Lin*\n\n19. **Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning.** arXiv 2022 [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.01992.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FCinà2022Wild.md)\n\n    *Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis, Sebastiano Vascon, Werner Zellinger, Bernhard Alois Moser, Alina Oprea, Battista Biggio, Marcello Pelillo, Fabio Roli*\n\n\n## Team Members\n\nThe project is maintained by \n\n*Natural Language Processing Lab., School of Computer Science and Engineering, Northeastern University*\n\n*NiuTrans Research*\n\nPlease feel free to contact us if you have any questions (libei_neu [at] outlook.com).\n\n## Acknowledge\n\nWe would like to thank the people who have contributed to this project. They are\n\n*Chuanhao Lv, Kaiyan Chang, Ziyang Wang, Shuhan Zhou, Nuo Xu, Bei Li, Yinqiao Li, Quan Du, Xin Zeng, Laohu Wang, Chenglong Wang, Xiaoqian Liu, Xuanjun Zhou, Jingnan Zhang, Yongyu Mu, Zefan Zhou, Yanhong Jiang, Xinyang Zhu, Xingyu Liu, Dong Bi, Ping Xu, Zijian Li, Fengning Tian, Hui Liu, Kai Feng, Yuhao Zhang, Chi Hu, Di Yang, Lei Zheng, Hexuan Chen, Zeyang Wang, Tengbo Liu, Xia Meng, Weiqiao Shan, Tao Zhou, Runzhe Cao, Yingfeng Luo, Binghao Wei, Wandi Xu, Yan Zhang, Yichao Wang, Mengyu Ma, Zihao Liu*\n","# 调查综述（NLP与ML）\n\n在本文档中，我们对自然语言处理（NLP）和机器学习（ML）领域的数百篇综述论文进行了梳理。我们将这些论文按热门主题进行分类，并针对一些有趣的问题进行了简单统计。此外，我们还列出了所有论文及其对应的URL链接（共1063篇）。\n\n:new: 大型语言模型综述列表已发布！[链接](https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurveyOfLLMs)\n\n## 分类\n\n我们参照近年来ACL和ICML的投稿指南，涵盖了NLP和ML领域的广泛方向。具体分类如下：\n\n### 自然语言处理\n+ \u003Ca href=\"#computational-social-science-and-social-media\">计算社会科学与社交媒体\u003C\u002Fa>\n+ \u003Ca href=\"#dialogue-and-interactive-systems\">对话与交互系统\u003C\u002Fa>\n+ \u003Ca href=\"#generation\">生成\u003C\u002Fa>\n+ \u003Ca href=\"#information-extraction\">信息抽取\u003C\u002Fa>\n+ \u003Ca href=\"#information-retrieval-and-text-mining\">信息检索与文本挖掘\u003C\u002Fa>\n+ \u003Ca href=\"#interpretability-and-analysis-of-models-for-nLP\">NLP模型的可解释性与分析\u003C\u002Fa>\n+ \u003Ca href=\"#knowledge-graph\">知识图谱\u003C\u002Fa>\n+ \u003Ca href=\"#language-grounding-to-vision-robotics-and-beyond\">语言与视觉、机器人等领域的跨模态理解\u003C\u002Fa>\n+ \u003Ca href=\"#large-language-models\">大型语言模型\u003C\u002Fa>\n+ \u003Ca href=\"#linguistic-theories-cognitive-modeling-and-psycholinguistics\">语言学理论、认知建模与心理语言学\u003C\u002Fa>\n+ \u003Ca href=\"#machine-learning-for-nlp\">面向NLP的机器学习\u003C\u002Fa>\n+ \u003Ca href=\"#machine-translation\">机器翻译\u003C\u002Fa>\n+ \u003Ca href=\"#named-entity-recognition\">命名实体识别\u003C\u002Fa>\n+ \u003Ca href=\"#natural-language-inference\">自然语言推理\u003C\u002Fa>\n+ \u003Ca href=\"#natural-language-processing\">自然语言处理\u003C\u002Fa>\n+ \u003Ca href=\"#nlp-applications\">NLP应用\u003C\u002Fa>\n+ \u003Ca href=\"#pre-trained-models\">预训练模型\u003C\u002Fa>\n+ \u003Ca href=\"#prompt\">提示工程\u003C\u002Fa>\n+ \u003Ca href=\"#question-answering\">问答\u003C\u002Fa>\n+ \u003Ca href=\"#reading-comprehension\">阅读理解\u003C\u002Fa>\n+ \u003Ca href=\"#recommender-systems\">推荐系统\u003C\u002Fa>\n+ \u003Ca href=\"#resources-and-evaluation\">资源与评估\u003C\u002Fa>\n+ \u003Ca href=\"#semantics\">语义学\u003C\u002Fa>\n+ \u003Ca href=\"#sentiment-analysis-stylistic-analysis-and-argument-mining\">情感分析、文体分析与论点挖掘\u003C\u002Fa>\n+ \u003Ca href=\"#speech-and-multimodality\">语音与多模态\u003C\u002Fa>\n+ \u003Ca href=\"#summarization\">摘要生成\u003C\u002Fa>\n+ \u003Ca href=\"#tagging-chunking-syntax-and-parsing\">标注、分块、句法分析与依存分析\u003C\u002Fa>\n+ \u003Ca href=\"#text-classification\">文本分类\u003C\u002Fa>\n\n### 机器学习\n+ \u003Ca href=\"#architectures\">架构\u003C\u002Fa>\n+ \u003Ca href=\"#automl\">AutoML\u003C\u002Fa>\n+ \u003Ca href=\"#bayesian-methods\">贝叶斯方法\u003C\u002Fa>\n+ \u003Ca href=\"#classification-clustering-and-regression\">分类、聚类与回归\u003C\u002Fa>\n+ \u003Ca href=\"#computer-vision\">计算机视觉\u003C\u002Fa>\n+ \u003Ca href=\"#contrastive-learning\">对比学习\u003C\u002Fa>\n+ \u003Ca href=\"#curriculum-learning\">课程学习\u003C\u002Fa>\n+ \u003Ca href=\"#data-augmentation\">数据增强\u003C\u002Fa>\n+ \u003Ca href=\"#deep-learning-general-methods\">深度学习通用方法\u003C\u002Fa>\n+ \u003Ca href=\"#deep-reinforcement-learning\">深度强化学习\u003C\u002Fa>\n+ \u003Ca href=\"#diffusion-models\">扩散模型\u003C\u002Fa>\n+ \u003Ca href=\"#federated-learning\">联邦学习\u003C\u002Fa>\n+ \u003Ca href=\"#few-shot-and-zero-shot-learning\">少样本与零样本学习\u003C\u002Fa>\n+ \u003Ca href=\"#general-machine-learning\">通用机器学习\u003C\u002Fa>\n+ \u003Ca href=\"#generative-adversarial-networks\">生成对抗网络\u003C\u002Fa>\n+ \u003Ca href=\"#graph-neural-networks\">图神经网络\u003C\u002Fa>\n+ \u003Ca href=\"#interpretability-and-analysis\">可解释性与分析\u003C\u002Fa>\n+ \u003Ca href=\"#knowledge-distillation\">知识蒸馏\u003C\u002Fa>\n+ \u003Ca href=\"#meta-learning\">元学习\u003C\u002Fa>\n+ \u003Ca href=\"#metric-learning\">度量学习\u003C\u002Fa>\n+ \u003Ca href=\"#ml-and-dl-applications\">ML与DL的应用\u003C\u002Fa>\n+ \u003Ca href=\"#model-compression-and-acceleration\">模型压缩与加速\u003C\u002Fa>\n+ \u003Ca href=\"#multi-label-learning\">多标签学习\u003C\u002Fa>\n+ \u003Ca href=\"#multi-task-and-multi-view-learning\">多任务与多视图学习\u003C\u002Fa>\n+ \u003Ca href=\"#online-learning\">在线学习\u003C\u002Fa>\n+ \u003Ca href=\"#optimization\">优化\u003C\u002Fa>\n+ \u003Ca href=\"#semi-supervised-weakly-supervised-and-unsupervised-learning\">半监督、弱监督与无监督学习\u003C\u002Fa>\n+ \u003Ca href=\"#transfer-learning\">迁移学习\u003C\u002Fa>\n+ \u003Ca href=\"#trustworthy-machine-learning\">可信机器学习\u003C\u002Fa>\n\n为减少类别间的不平衡，我们从ACL和ICML原始分类中分离出了一些热门子领域。例如，命名实体识别在我们的分类中被列为一级领域，因为它已成为多篇综述的重点。\n\n## 统计数据\n\n我们在图1和图2中展示了各领域的论文数量。\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_11989a8f8e54.png\" width=\"70%\" height=\"70%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">图1：各NLP领域论文数量。\u003C\u002Fp>\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_8b289edf853b.png\" width=\"70%\" height=\"70%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">图2：各ML领域论文数量。\u003C\u002Fp>\n\n此外，我们还绘制了论文数量随发表年份变化的曲线图（见图3）。\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_6a09c708d052.png\" width=\"70%\" height=\"70%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">图3：论文数量与发表年份的关系。\u003C\u002Fp>\n\n另外，我们还生成了词云图来展示这些综述中的热点话题（见图4和图5）。\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_a57e935ae60d.png\" width=\"60%\" height=\"60%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">图4：NLP领域的词云图。\u003C\u002Fp>\n\n\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_readme_d498dc88fb3a.png\" width=\"60%\" height=\"60%\"\u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">图5：ML领域的词云图。\u003C\u002Fp>\n\n## NLP论文列表\n\n#### [计算社会科学与社交媒体](#content)\n\n1. **基于深度学习的社区发现综合综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.12584.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FSu2021A.md)\n\n    *苏星、薛珊、刘凡振、吴佳、杨健、周川、胡文斌、塞西尔·帕里斯、苏里亚·尼泊尔、金迪、沈泉志、菲利普·S·余*\n\n2. **假新闻综述：基础理论、检测方法及未来机遇。** ACM Comput. Surv. 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.00315) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FZhou2021A.md)\n\n    *周信义、雷扎·扎法拉尼*\n\n3. **自然语言处理中的种族、种族主义与反种族主义研究综述。** ACL 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.11410) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FField2021A.md)\n\n    *安贾莉·菲尔德、苏琳·布洛杰特、泽拉克·瓦西姆、尤利娅·茨韦特科夫*\n\n4. **计算传播学检测研究综述。** IJCAI 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.08024.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FMartino2020A.md)\n\n    *乔瓦尼·达·圣马蒂诺、斯特凡诺·克雷西、阿尔贝托·巴隆-塞德尼奥、俞承赫、罗伯托·迪·皮耶特罗、普雷斯拉夫·纳科夫*\n\n5. **在线社交网络中的信任预测研究综述。** IEEE Access 2020 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9142365) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FGhafari2020A.md)\n\n    *赛义德·莫赫森·加法里、阿敏·贝赫什蒂、阿迪提亚·乔希、塞西尔·帕里斯、阿德南·马哈茂德、沙赫帕尔·亚赫奇、梅赫梅特·A·奥尔贡*\n\n6. **计算社会语言学：研究综述。** Comput. Linguistics 2016 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1508.07544) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FNguyen2016Computational.md)\n\n    *董阮、A·塞扎·多格鲁兹、卡罗琳·P·罗斯、弗兰西斯卡·德·容*\n\n7. **应对网络辱骂语言：基于伦理与人权视角的综述。** J. Artif. Intell. Res. 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.12305) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FKiritchenko2021Confronting.md)\n\n    *斯维特兰娜·基里琴科、伊萨尔·内贾德戈利、凯瑟琳·C·弗雷泽*\n\n8. **从符号到嵌入：计算社会科学中两种表示方法的比较。** J. Soc. Comput. 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.14198) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FChen2021From.md)\n\n    *陈慧敏、杨成、张轩明、刘志远、孙茂松、金建斌*\n\n9. **语言（技术）即权力：自然语言处理中“偏见”的批判性综述。** ACL 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.14050) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FBlodgett2020Language.md)\n\n    *苏琳·布洛杰特、索伦·巴罗卡斯、哈尔·达乌梅三世、汉娜·M·沃拉奇*\n\n10. **语言生成中的社会偏见：进展与挑战。** ACL 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.04054.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FSheng2021Societal.md)\n\n    *艾米丽·盛、蔡铠伟、普雷姆·纳塔拉詹、彭楠云*\n\n11. **应对网络滥用：自动化滥用检测方法综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.06024.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FMishra2019Tackling.md)\n\n    *普什卡尔·米什拉、海伦·扬纳库达基斯、叶卡捷琳娜·舒托娃*\n\n12. **词嵌入能否准确反映我们对人的信念调查？** ACL 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.12043) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FComputational-Social-Science-and-Social-Media\u002FJoseph2020When.md)\n\n    *肯尼思·约瑟夫、乔纳森·H·摩根*\n\n#### [对话与交互系统](#content)\n\n1. **面向自发对话与即时消息的阿拉伯语对话理解研究综述。** arXiv 2015 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1505.03084) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FElmadany2015A.md)\n\n    *阿卜杜勒拉希姆·A·埃尔马达尼、谢里夫·M·阿卜杜、梅尔瓦特·盖斯*\n\n2. **构建数据驱动对话系统的可用语料库研究综述：期刊版。** Dialogue Discourse 2018 [论文](https:\u002F\u002Fjournals.uic.edu\u002Fojs\u002Findex.php\u002Fdad\u002Farticle\u002Fview\u002F10733\u002F9501) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FSerban2018A.md)\n\n    *尤利安·弗拉德·塞尔班、瑞安·洛厄、彼得·亨德森、洛朗·夏尔林、乔埃尔·皮诺*\n\n3. **文档增强型对话系统（DGDS）研究综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.13818) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FMa2020A.md)\n\n    *马龙轩、张伟楠、李明达、刘婷*\n\n4. **面向任务导向对话的意图分类与槽位填充数据集研究综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.13211.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FLarson2022A.md)\n\n    *斯特凡·拉尔森、凯文·利奇*\n\n5. **以对话系统为中心的自然语言生成技术研究综述——过去、现在与未来方向。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.00500) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FSanthanam2019A.md)\n\n    *萨尚克·桑塔南、萨米拉·谢赫*\n\n6. **用于自动会话分析的神经网络模型研究综述：迈向社会科学的更好融合。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.16891.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FClavel2022A.md)\n\n    *克洛伊·克拉维尔、马蒂厄·拉博、朱斯汀·卡塞尔*\n\n7. **对话管理研究综述：最新进展与挑战。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.02233) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FDai2020A.md)\n\n    *戴银培、于慧华、蒋一轩、唐成光、李永彬、孙健*\n\n8. **对话系统研究综述：最新进展与新前沿。** SIGKDD Explor. 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.01731) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FChen2017A.md)\n\n    *陈洪深、刘晓睿、尹大伟、唐继良*\n\n9. **多轮对话理解的进展：研究综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03125) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FZhang2021Advances.md)\n\n    *张卓胜、赵海*\n\n10. **构建智能开放域对话系统的挑战。** ACM Trans. Inf. Syst. 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.05709) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FHuang2020Challenges.md)\n\n    *黄民列、朱小燕、高剑峰*\n\n11. **会话代理：理论与应用。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.03164.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FWahde2022Conversational.md)\n\n    *马蒂亚斯·瓦德、马可·维尔戈林*\n\n12. **会话式机器阅读理解：文献综述。** COLING 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.00671) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FGupta2020Conversational.md)\n\n    *索米尔·古普塔、巴努·普拉塔普·辛格·拉瓦特、洪宇*\n\n13. **如何评估你的对话模型：方法回顾。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.01369.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FLi2021How.md)\n\n    *李鑫萌、吴万森、秦龙、尹全俊*\n\n14. **对话式人工智能的神经方法。** ACL 2018 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.08267) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FGao2018Neural.md)\n\n    *高建峰、米歇尔·加利、李立宏*\n\n15. **对话式人工智能的神经方法：问答、任务导向型对话与社交聊天机器人。** Now Foundations and Trends 2019 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8649787) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FGao2019Neural.md)\n\n    *高建峰、米歇尔·加利、李立宏*\n\n16. **基于POMDP的统计语音对话系统：综述。** IEEE会议论文集 2013 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6407655\u002F) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FYoung2013POMDP-Based.md)\n\n    *史蒂夫·J·杨、米莉察·加西奇、布莱斯·汤姆森、杰森·D·威廉姆斯*\n\n17. **任务导向型对话系统的最新进展与挑战。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.07490) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FZhang2020Recent.md)\n\n    *张铮、高信隆、黄敏列、朱小燕*\n\n18. **基于深度学习的对话系统最新进展：系统性综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.04387.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FNi2021Recent.md)\n\n    *倪金杰、汤姆·杨、弗拉德·潘德利亚、薛福照、维奈·阿迪加、埃里克·坎布里亚*\n\n19. **话语级对话理解：一项实证研究。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.13902) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FDialogue-and-Interactive-Systems\u002FGhosal2020Utterance-level.md)\n\n    *迪潘韦·戈萨尔、纳沃尼尔·马久姆达尔、拉达·米哈尔切亚、苏贾尼亚·波里亚*\n\n#### [生成](#content)\n\n1. **基于Transformer预训练语言模型的可控文本生成综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.05337.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FZhang2022A.md)\n\n    *张汉青、宋浩林、李绍宇、周明、宋大伟*\n\n2. **知识增强型文本生成综述。** ACM计算综述 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.04389.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FYu2022A.md)\n\n    *于文豪、朱成光、李在堂、胡志婷、王清云、季恒、蒋萌*\n\n3. **多跳问答与生成综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.09140.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FMavi2022A.md)\n\n    *瓦伊巴夫·马维、阿努巴夫·詹格拉、亚当·雅托特*\n\n4. **检索增强型文本生成综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.01110.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FLi2022A.md)\n\n    *李华阳、苏一轩、蔡登、王燕、刘乐茂*\n\n5. **文本简化综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.08612) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FSikka2020A.md)\n\n    *普纳尔迪普·西卡、维杰·马戈*\n\n6. **机器生成文本的自动检测：批判性综述。** COLING 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.01314.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FJawahar2020Automatic.md)\n\n    *加内什·贾瓦哈尔、穆罕默德·阿卜杜勒-马吉德、拉克斯·V·S·拉克什曼南*\n\n7. **自动故事生成：挑战与尝试。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.12634) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FAlabdulkarim2021Automatic.md)\n\n    *阿玛尔·阿尔阿卜杜勒卡里姆、李思彦、彭湘宇*\n\n8. **ChatGPT并非万能：大型生成式AI模型的现状综述。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.04655.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FGozalo-Brizuela2023ChatGPT.md)\n\n    *罗伯托·戈萨洛-布里苏埃拉、爱德华多·C·加里多-梅尔尚*\n\n9. **数据到文本系统中的内容选择：综述。** arXiv 2016 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.08375) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FGkatzia2016Content.md)\n\n    *迪米特拉·加茨娅*\n\n10. **数据驱动的句子简化：综述与基准测试。** 计算语言学 2020 [论文](https:\u002F\u002Fwww.mitpressjournals.org\u002Fdoi\u002Fpdf\u002F10.1162\u002FCOLI_a_00370) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FAlva-Manchego2020Data-Driven.md)\n\n    *费尔南多·阿尔瓦-曼切戈、卡罗丽娜·斯卡顿、露西亚·斯佩恰*\n\n11. **用于文本风格转换的深度学习：综述。** 计算语言学 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.00416.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FJin2022Deep.md)\n\n    *迪·金、智静·金、胡志婷、奥尔加·韦赫托莫娃、拉达·米哈尔切亚*\n\n12. **文本生成评估：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.14799) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FCelikyilmaz2020Evaluation.md)\n\n    *阿斯莉·切利基尔马兹、伊丽莎白·克拉克、高建峰*\n\n13. **创意NLG系统的用户评估：近期论文的跨学科综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.00308.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FHämäläinen2021Human.md)\n\n    *米卡·海马莱宁、哈立德·阿尔-纳贾尔*\n\n14. **关键词生成：多角度综述。** FRUCT 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.05059) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FÇano2019Keyphrase.md)\n\n    *埃里翁·恰诺、翁德雷·博雅尔*\n\n15. **神经网络语言生成：框架、方法与评估。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.15780.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FGarbacea2020Neural.md)\n\n    *克里斯蒂娜·加尔巴塞亚、梅巧竹*\n\n16. **神经网络文本生成：过去、现在与未来。** arXiv 2018 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.07133.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FLu2018Neural.md)\n\n    *卢思迪、朱耀明、张伟楠、王军、余勇*\n\n17. **新闻报道的测验式问题生成。** WWW 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.09094) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FLelkes2021Quiz-Style.md)\n\n    *阿达姆·D·莱尔克斯、阮文庆、于聪*\n\n18. **神经网络问题生成的最新进展。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.08949) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FPan2019Recent.md)\n\n    *梁明·潘、雷文强、查塔·桑森、简敏延*\n\n19. **SQL查询生成的最新进展：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07667) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FKalajdjieski2020Recent.md)\n\n    *约万·卡拉吉耶斯基、马蒂娜·托舍夫斯卡、弗罗西娜·斯托亚诺夫斯卡*\n\n20. **自然语言生成中的幻觉现象综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.03629.pdf) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FJi2022Survey.md)\n\n    *季子威、李娜妍、丽塔·弗里斯克、于铁正、苏丹、徐燕、石井悦子、方艺珍、安德烈娅·马多托、帕斯卡尔·冯*\n\n21. **自然语言生成领域的现状综述：核心任务、应用与评估。** J. Artif. Intell. Res. 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.09902) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FGeneration\u002FGatt2018Survey.md)\n\n    *阿尔伯特·加特，埃米尔·克拉默*\n\n#### [信息抽取](#content)\n\n1. **事实提取与验证综述。** ACM Comput. Surv. 2023 [论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F2010.03001) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FBekoulis2023A.md)\n\n    *扬尼斯·贝库利斯，克里斯蒂娜·帕帕扬诺普卢，尼科斯·德利吉安尼斯*\n\n2. **关系抽取深度学习方法综述。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.03645) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FKumar2017A.md)\n\n    *桑塔努·库马尔*\n\n3. **文本事件抽取综述。** IEEE Access 2019 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8918013) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FXiang2019A.md)\n\n    *魏翔，王邦*\n\n4. **面向决策支持系统的文本事件抽取方法综述。** Decis. Support Syst. 2016 [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0167923616300173) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FHogenboom2016A.md)\n\n    *弗雷德里克·霍根布姆，弗拉维乌斯·弗拉辛卡尔，乌扎伊·凯马克，弗兰西斯卡·德容，埃米尔·卡隆*\n\n5. **自然语言理解中意图检测与槽位填充联合模型的综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08091) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FWeld2021A.md)\n\n    *亨利·韦尔德，黄晓琪，龙思齐，波恩·乔西亚，韩素妍·卡伦*\n\n6. **社交网络中文本事件抽取综述。** LPKM 2017 [论文](http:\u002F\u002Fceur-ws.org\u002FVol-1988\u002FLPKM2017_paper_15.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FMejri2017A.md)\n\n    *穆罕默德·梅日里，贾莱勒·阿凯奇*\n\n7. **深度学习事件抽取：方法与应用综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.02126.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FLi2021A.md)\n\n    *钱莉，李建新，盛嘉伟，崔世尧，吴佳，何一鸣，彭浩，郭树，王丽红，阿敏·贝赫什蒂，菲利普·S·余*\n\n8. **开放信息抽取综述。** COLING 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.05599) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FNiklaus2018A.md)\n\n    *克里斯蒂娜·尼克劳斯，马蒂亚斯·塞托，安德烈·弗雷塔斯，齐格弗里德·汉施胡*\n\n9. **用于从文本中提取时间信息的时间推理综述（扩展摘要）。** IJCAI 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.06527) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FLeeuwenberg2020A.md)\n\n    *阿图尔·勒文伯格，玛丽-弗朗辛·蒙斯*\n\n10. **文本事件抽取概述。** DeRiVE@ISWC 2011 [论文](http:\u002F\u002Fceur-ws.org\u002FVol-779\u002Fderive2011_submission_1.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FHogenboom2011An.md)\n\n    *弗雷德里克·霍根布姆，弗拉维乌斯·弗拉辛卡尔，乌扎伊·凯马克，弗兰西斯卡·德容*\n\n11. **从自然语言文本中自动提取因果关系：全面综述。** arXiv 2016 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1605.07895) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FAsghar2016Automatic.md)\n\n    *纳比哈·阿斯加尔*\n\n12. **复杂关系抽取：挑战与机遇。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.04821.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FJiang2020Complex.md)\n\n    *江海云，鲍巧本，程桥，杨德庆，王力，肖阳华*\n\n13. **从文本中提取事件及其关系：近期研究进展与挑战综述。** AI Open 2020 [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS266665102100005X\u002Fpdfft?md5=3983861e9ae91ce7b45f0c5533071077&pid=1-s2.0-S266665102100005X-main.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FLiu2020Extracting.md)\n\n    *刘康，陈宇博，刘健，左欣宇，赵俊*\n\n14. **低资源场景下的知识抽取：综述与展望。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.08063.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FDeng2022Knowledge.md)\n\n    *邓淑敏，张宁宇，陈辉，熊飞宇，杰夫·Z·潘，陈华军*\n\n15. **更多数据、更多关系、更多上下文与更开放：关系抽取的回顾与展望。** AACL 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.03186) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FHan2020More.md)\n\n    *许涵，高天宇，林燕凯，彭浩，杨耀良，肖超俊，刘志远，李鹏，周杰，孙茂松*\n\n16. **神经关系抽取：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.04247) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FAydar2020Neural.md)\n\n    *梅赫梅特·艾达尔，奥兹盖·博扎尔，富尔坎·厄兹拜*\n\n17. **无模式，无识别：关于文本聚类与主题建模的可重复性及偏差问题的综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.01712.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FSilva2022No.md)\n\n    *玛丽利亚·科斯塔·罗森多·席尔瓦，费利佩·阿尔维斯·西奎埃拉，若昂·佩德罗·曼托瓦尼·塔雷加，若昂·维托尔·帕塔卡·贝诺蒂，奥古斯托·索萨·努内斯，米格尔·德·马托斯·加尔迪尼，维尼修斯·阿道尔福·佩雷拉·达·席尔瓦，纳迪娅·费利克斯·费利佩·达·席尔瓦，安德烈·卡洛斯·庞塞·德·莱昂·费雷拉·德·卡瓦略*\n\n18. **面向任务型对话系统的槽位填充与意图分类近期神经方法：综述。** COLING 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.00564) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FLouvan2020Recent.md)\n\n    *塞缪尔·卢万，贝尔纳多·马尼尼*\n\n19. **关系抽取：综述。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.05191) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FPawar2017Relation.md)\n\n    *萨钦·帕瓦尔，吉里什·K·帕尔希卡尔，普什帕克·巴塔查里亚*\n\n20. **实体与关系联合抽取技术：综述。** CICLing 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.06118) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Extraction\u002FPawar2019Techniques.md)\n\n    *萨钦·帕瓦尔，普什帕克·巴塔查里亚，吉里什·K·帕尔希卡尔*\n\n#### [信息检索与文本挖掘](#content)\n\n1. **文本挖掘简要综述：分类、聚类与抽取技术。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.02919) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FAllahyari2017A.md)\n\n    *梅赫迪·阿拉亚里，赛义德·阿明·普里耶，梅赫迪·阿塞菲，赛义德·萨法伊，伊丽莎白·D·特里普，胡安·B·古铁雷斯，克里斯·J·科楚特*\n\n2. **缓解高度多语言文本挖掘应用开发的方法综述。** Lang. Resour. Evaluation 2012 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1401.2937) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FSteinberger2012A.md)\n\n    *拉尔夫·施泰因贝格*\n\n3. **检索增强型文本生成综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.01110.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FLi2022A.md)\n\n    *李华阳、苏一轩、蔡登、王燕、刘乐茂*\n\n4. **21世纪的数据挖掘与信息检索：文献综述。** Comput. Sci. Rev. 2019 [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1574013719301297) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FLiu2019Data.md)\n\n    *刘佳颖、孔翔杰、周新宇、王磊、张达、伊万·李、徐博、夏峰*\n\n5. **基于预训练语言模型的密集文本检索：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.14876.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FZhao2022Dense.md)\n\n    *赵欣伟、刘静、任瑞阳、文继荣*\n\n6. **神经实体链接：基于深度学习的模型综述。** Semantic Web 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.00575) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FSevgili2022Neural.md)\n\n    *厄兹盖·塞夫吉利、阿特姆·谢尔马诺夫、米哈伊尔·Y·阿尔希波夫、亚历山大·潘琴科、克里斯·比曼*\n\n7. **用于信息检索的神经网络模型。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.01509.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FMitra2017Neural.md)\n\n    *巴斯卡尔·米特拉、尼克·克拉斯韦尔*\n\n8. **观点挖掘与分析：综述。** arXiv 2013 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1307.3336) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FBuche2013Opinion.md)\n\n    *阿蒂·布切、M. B. 钱达克、阿克沙伊·扎德高恩卡*\n\n9. **信息检索中的预训练方法。** Found. Trends Inf. Retr. 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.13853.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FFan2022Pre-training.md)\n\n    *范义兴、谢晓辉、蔡银琼、陈佳、马鑫宇、李向生、张如清、郭家锋*\n\n10. **上下文语言模型中的关系世界知识表示：综述。** EMNLP 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.05837) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FSafavi2021Relational.md)\n\n    *塔拉·萨法维、达奈·库特拉*\n\n11. **短文本主题建模技术、应用及性能：综述。** IEEE Trans. Knowl. Data Eng. 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.07695) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FQiang2022Short.md)\n\n    *强继鹏、钱振宇、李云、袁云浩、吴新东*\n\n12. **将搜索带入任务领域。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.05046.pdf) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FShah2023Taking.md)\n\n    *奇拉格·沙赫、赖恩·W·怀特、保罗·托马斯、巴斯卡尔·米特拉、绍温·萨卡尔、尼古拉斯·J·贝尔金*\n\n13. **主题建模与深度神经网络的结合：综述。** IJCAI 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00498) [bib](\u002Fbib\u002FNatural-Language-Processing\u002FInformation-Retrieval-and-Text-Mining\u002FZhao2021Topic.md)\n\n    *何兆、丁Q·冯、越辉、袁进、兰杜、雷·L·邦廷*\n\n#### [NLP模型的可解释性与分析](#content)\n\n1. **BERT学入门：我们所知的BERT工作机制。** Trans. Assoc. Comput. 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Linguistics 2014 [论文](https:\u002F\u002Fdirect.mit.edu\u002Fcoli\u002Farticle\u002F40\u002F2\u002F469\u002F1475\u002FA-Survey-of-Arabic-Named-Entity-Recognition-and) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FNamed-Entity-Recognition\u002FShaalan2014A.md)\n\n    *哈立德·沙兰*\n\n2. **命名实体识别与分类综述。** Lingvisticae Investigationes 2007 [论文](https:\u002F\u002Fnlp.cs.nyu.edu\u002Fsekine\u002Fpapers\u002Fli07.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FNamed-Entity-Recognition\u002FNadeau2007A.md)\n\n    *大卫·纳迪欧、关根聪*\n\n3. **阿萨姆语及其他印度语言中的命名实体识别综述。** arXiv 2014 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1407.2918) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FNamed-Entity-Recognition\u002FTalukdar2014A.md)\n\n    *吉蒂莫尼·塔卢克达尔、普兰贾尔·普罗蒂姆·博拉、阿鲁普·巴鲁阿*\n\n4. **基于深度学习的命名实体识别综述。** IEEE Trans. Knowl. 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Intell. Mag. 2014 [论文](http:\u002F\u002Fkrchowdhary.com\u002Fai\u002Fai14\u002Flects\u002Fnlp-research-com-intlg-ieee.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FCambria2014Jumping.md)\n\n    *埃里克·坎布里亚、贝博·怀特*\n\n26. **自然语言处理中的元学习综述。** NAACL-HLT 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.01500.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FLee2022Meta.md)\n\n    *李鸿毅、李尚文、武堂*\n\n27. **自然语言处理——综述。** arXiv 2012 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1209.6238) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FNatural-Language-Processing\u002FMote2012Natural.md)\n\n    *凯文·莫特*\n\n28. **自然语言处理：现状、趋势与挑战。** Multim. 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Lastra-Díaz, Ana García-Serrano*\n\n2. **词嵌入与基于本体的方法在词语相似度上的可复现调查：线性组合优于现有最佳方法。** 工程应用人工智能 2019 [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0952197619301745) [BibTeX](\u002Fbib\u002FNatural-Language-Processing\u002FSemantics\u002FLastra-Díaz2019A.md)\n\n*胡安·J·拉斯特拉-迪亚斯、何苏·戈伊科埃切亚、穆罕默德·阿里·哈吉·泰布、安娜·加西亚-塞拉诺、穆罕默德·本·阿乌伊查、埃内科·阿吉雷*\n\n3. **语义分割损失函数综述。** CIBCB 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.14822) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSemantics\u002FJadon2020A.md)\n\n    *舒鲁蒂·贾登*\n\n4. **释义与文本蕴涵方法综述。** J. Artif. Intell. 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Hovy*\n\n11. **细粒度金融观点挖掘：综述与研究议程。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.01897.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FChen2020Fine-grained.md)\n\n    *陈忠志、黄亨申、陈信希*\n\n12. **关于负面评论中的积极偏见。** ACL 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.12056) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FAithal2021On.md)\n\n    *马杜苏丹·艾塔尔、陈浩*\n\n13. **讽刺检测：一项比较研究。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.02276) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FYaghoobian2021Sarcasm.md)\n\n    *哈梅德·亚古比安、哈米德·R·阿拉伯尼亚、哈立德·拉希德*\n\n14. **情感分析算法与应用：综述。** Ain Shams Engineering Journal 2014 [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2090447914000550) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FMedhat2014Sentiment.md)\n\n    *瓦拉·梅达特、艾哈迈德·哈桑、霍达·科拉希*\n\n15. **阿拉伯语的情感分析：方法与技术简要综述。** arXiv 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02782) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FAlrefai2018Sentiment.md)\n\n    *穆阿斯·阿尔雷法伊、侯萨姆·法里斯、易卜拉欣·阿尔贾拉赫*\n\n16. **捷克语文本的情感分析：算法综述。** ICAART 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.02780) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FÇano2019Sentiment.md)\n\n    *埃里翁·恰诺、翁德热伊·博雅尔*\n\n17. **Twitter数据的情感分析：技术综述。** IJCAI 2016 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.06971) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FVishal.A.Kharde2016Sentiment.md)\n\n    *维沙尔·A·卡尔德、希塔尔·索纳万教授*\n\n18. **YouTube上的情感分析：简要综述。** arXiv 2015 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.09142) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FAsghar2015Sentiment.md)\n\n    *穆罕默德·祖拜尔·阿斯加尔、沙基尔·艾哈迈德、阿芙莎娜·马尔瓦特、法扎尔·马苏德·昆迪*\n\n19. **非英语语言的情感\u002F主观性分析综述。** Soc. 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Min. 2016 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.00087) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FKorayem2016Sentiment.md)\n\n    *穆罕默德·科拉耶姆、哈利法·阿尔贾达、大卫·J·克兰达尔*\n\n20. **基于方面的情感分析数据集综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.05232.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FChebolu2022Survey.md)\n\n    *西瓦·乌代·桑普里特·切博卢、弗兰克·德农库尔、内迪姆·利普卡、塔玛尔·索洛里奥*\n\n21. **面向社会公益的论点挖掘：综述。** ACL 2021 [论文](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.107.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FVecchi2021Towards.md)\n\n    *伊娃·玛丽亚·韦奇、妮蕾·法尔克、伊曼·朱恩迪、加布里埃拉·拉佩萨*\n\n22. **用于情感分析的词嵌入：全面的经验研究综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.00753) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSentiment-Analysis,-Stylistic-Analysis-and-Argument-Mining\u002FÇano2019Word.md)\n\n    *埃里翁·恰诺、毛里齐奥·莫里西奥*\n\n#### [语音与多模态](#content)\n\n1. **手语识别技术与算法的比较分析。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13941) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FKumar2023A.md)\n\n    *鲁佩什·库马尔、阿尤什·辛哈、阿舒托什·巴杰帕伊、S. K.辛格*\n\n2. **跨模态检索综合调查。** arXiv 2016 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1607.06215) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FWang2016A.md)\n\n    *王凯业、尹琪悦、王伟、吴树、王亮*\n\n3. **多模态表情包分类：综述与开放研究问题。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.08395) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FAfridi2020A.md)\n\n    *塔里克·哈比卜·阿夫里迪、阿夫塔布·阿拉姆、穆罕默德·努曼·汗、贾瓦德·汗、李荣九*\n\n4. **AMR到文本的神经网络综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.07328.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FHao2022A.md)\n\n    *郝洪宇、李广通、胡志明、王华峰*\n\n5. **文本到图像合成中对抗性神经网络的综述与分类体系。** WIREs Data Mining Knowl. Discov. 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.09399) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FAgnese2020A.md)\n\n    *豪尔赫·阿格涅斯、乔纳森·埃雷拉、海成·陶、邢泉·朱*\n\n6. **代码转换语音与语言处理综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.00784) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FSitaram2019A.md)\n\n    *苏纳亚娜·西塔拉姆、卡亚蒂·拉加维·钱杜、赛·克里希纳·拉拉班迪、艾伦·W·布莱克*\n\n7. **OCR与文档理解的深度学习方法综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.13534.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FSubramani2020A.md)\n\n    *尼尚特·苏布拉马尼、亚历山大·马顿、马尔科姆·格里夫斯、艾德里安·拉姆*\n\n8. **TIMIT电话识别任务中近期DNN架构的综述。** TSD 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07974) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FMichálek2018A.md)\n\n    *约瑟夫·米哈莱克、扬·瓦内克*\n\n9. **视觉-语言预训练模型综述。** IJCAI 2022 [论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0762.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FDu2022A.md)\n\n    *杜一凡、刘子康、李俊毅、韦恩·辛·赵*\n\n10. **语音翻译方法学综述——声学方言解码器。** arXiv 2016 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.03934) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FKrupakar2016A.md)\n\n    *汉斯·克鲁帕卡尔、基尔蒂卡·拉杰维尔、巴拉蒂·B、安吉尔·黛博拉·S、瓦利德维·克里希纳穆提*\n\n11. **神经语音合成综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.15561.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FTan2021A.md)\n\n    *徐坦、秦涛、弗兰克·K·宋、刘铁燕*\n\n12. **口语理解研究：最新进展与新前沿。** IJCAI 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03095) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FQin2021A.md)\n\n    *秦立波、谢天宝、车万翔、刘挺*\n\n13. **图像字幕生成中深度学习方法的全面综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.13114.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FElhagry2021A.md)\n\n    *艾哈迈德·埃尔哈格里、卡里玛·卡达维*\n\n14. **带有口音的语音识别：综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.10747) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FHinsvark2021Accented.md)\n\n    *阿瑟·欣斯瓦克、娜塔莉·德尔沃思、米格尔·德尔里奥、昆廷·麦克纳马拉、乔舒亚·董、瑞安·韦斯特曼、米歇尔·黄、约瑟夫·帕拉卡皮利、珍妮弗·德雷克斯勒、伊利亚·皮尔金、尼沙尔·班达里、米格尔·杰特*\n\n15. **基于图像的自动描述生成：模型、数据集及评估指标综述。** J. Artif. Intell. Res. 2016 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.03896) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FBernardi2016Automatic.md)\n\n    *拉斐拉·贝尔纳迪、鲁凯特·恰基奇、德斯蒙德·埃利奥特、艾库特·埃尔德姆、埃尔库特·埃尔德姆、纳兹莉·伊基兹勒尔-钦比斯、弗兰克·凯勒、阿德里安·穆斯卡特、芭芭拉·普兰克*\n\n16. **有限词汇量下的自动语音识别：综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.10254.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FFendji2021Automatic.md)\n\n    *让·路易·K. E. 芬吉、黛安·C. M. 塔拉、布莱斯·O. 燕克、马塞林·阿滕肯*\n\n17. **利用面部、语音和文本线索在动态数据中进行深度情感识别：综述。** TechRxiv 2021 [论文](https:\u002F\u002Fwww.techrxiv.org\u002Farticles\u002Fpreprint\u002FDeep_Emotion_Recognition_in_Dynamic_Data_using_Facial_Speech_and_Textual_Cues_A_Survey\u002F15184302\u002F1) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FZhang2021Deep.md)\n\n    *张涛、谭振华*\n\n18. **基于深度学习方法的图像字幕生成：综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.08110) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FWang2019Image.md)\n\n    *王一宇、徐俊刚、孙英飞、何奔*\n\n19. **视听情境下的学习：回顾、分析与新视角。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.09579.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FWei2022Learning.md)\n\n    *魏亚科、胡迪、田亚鹏、李学龙*\n\n20. **多模态智能：表示学习、信息融合及应用。** IEEE J. Sel. Top. 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Multim. 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.09554) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FQiao2021Referring.md)\n\n    *严远桥、晁锐邓、齐武*\n\n27. **基于深度学习的端到端语音合成技术综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.09995) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FMu2021Review.md)\n\n    *穆兆熙、杨鑫宇、董一卓*\n\n28. **语音与语言处理。** 斯坦福大学 2019 [论文](http:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FJurafsky2019Speech.md)\n\n    *丹·朱拉夫斯基、詹姆斯·H·马丁*\n\n29. **基于Transformer的视频-语言预训练综述。** AI Open 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.09920.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FRuan2022Survey.md)\n\n    *陆丹·阮、秦晋*\n\n30. **野外场景下的文本检测与识别：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.04305) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FRaisi2020Text.md)\n\n    *佐拜尔·赖西、穆罕默德·A·奈尔、保罗·W·菲古斯、史蒂文·沃德尔、约翰·S·泽莱克*\n\n31. **野外场景下的文本识别：综述。** ACM Comput. 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Vis. Image Underst. 2017 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1607.05910.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FWu2017Visual.md)\n\n    *Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony R. Dick, Anton van den Hengel*\n\n38. **视觉问答：数据集、算法及未来挑战。** Comput. Vis. Image Underst. 2017 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1610.01465.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSpeech-and-Multimodality\u002FKafle2017Visual.md)\n\n    *Kushal Kafle, Christopher Kanan*\n\n39. **VLP：视觉-语言预训练综述。** Int. J. Autom. 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Linguistics 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12515.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FWang2022A.md)\n\n    *Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie Zhou*\n\n3. **对话摘要综述：最新进展与新前沿。** IJCAI 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.03175) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FFeng2022A.md)\n\n    *Xiachong Feng, Xiaocheng Feng, Bing Qin*\n\n4. **基于神经网络的摘要方法综述。** arXiv 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04589) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FDong2018A.md)\n\n    *Yue Dong*\n\n5. **抽象会议摘要综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.04163.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FRennard2022Abstractive.md)\n\n    *Virgile Rennard, Guokan Shang, Julie Hunter, Michalis Vazirgiannis*\n\n6. **抽象摘要：现状综述。** AAAI 2019 [论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F5056) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FLin2019Abstractive.md)\n\n    *Hui Lin, Vincent Ng*\n\n7. **自动文摘与循证医学：两个领域的综述。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.08162) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FSarker2017Automated.md)\n\n    *Abeed Sarker, Diego Mollá Aliod, Cécile Paris*\n\n8. **用于文本摘要的自动关键词提取：综述。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.03242) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FBharti2017Automatic.md)\n\n    *Santosh Kumar Bharti, Korra Sathya Babu*\n\n9. **科技文章自动摘要：综述。** J. King Saud Univ. Comput. Inf. Sci. 2022 [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1319157820303554) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FAltmami2022Automatic.md)\n\n    *Nouf Ibrahim Altmami, Mohamed El Bachir Menai*\n\n10. **基于深度学习的抽象文本摘要：方法、数据集、评估指标与挑战。** Mathematical Problems in Engineering 2020 [论文](https:\u002F\u002Fwww.hindawi.com\u002Fjournals\u002Fmpe\u002F2020\u002F9365340\u002F) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FSuleiman2020Deep.md)\n\n    *Dima Suleiman, Arafat Awajan*\n\n11. **从标准摘要到新任务及其他：基于流形信息的摘要。** IJCAI 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.04684) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FGao2020From.md)\n\n    *Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan*\n\n12. **如何评估摘要生成器：人工语言质量评估的设计与统计分析。** EACL 2021 [论文](https:\u002F\u002Faclanthology.org\u002F2021.eacl-main.160.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FSteen2021How.md)\n\n    *Julius Steen, Katja Markert*\n\n13. **知识感知文档摘要：知识、嵌入方法与架构综述。** Knowl. Based Syst. 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.11190.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FQu2022Knowledge-aware.md)\n\n    *Yutong Qu, Wei Emma Zhang, Jian Yang, Lingfei Wu, Jia Wu*\n\n14. **基于深度学习技术的多文档摘要：综述。** ACM Comput. Surv. 2023 [论文](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.04843.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FMa2023Multi-document.md)\n\n    *Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, Quan Z. Sheng*\n\n15. **使用序列到序列模型的神经抽象文本摘要。** Trans. Data Sci. 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.02303) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FShi2021Neural.md)\n\n    *Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy*\n\n16. **近期自动文摘技术综述。** Artif. Intell. Rev. 2017 [论文](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007%2Fs10462-016-9475-9) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FGambhir2017Recent.md)\n\n    *Mahak Gambhir, Vishal Gupta*\n\n17. **文本摘要技术简要综述。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.02268) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FAllahyari2017Text.md)\n\n    *Mehdi Allahyari, Seyed Amin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys J. Kochut*\n\n18. **抽象文本摘要中的事实不一致性问题：综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.14839) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FHuang2021The.md)\n\n    *Yi-Chong Huang, Xia-Chong Feng, Xiao-Cheng Feng, Bing Qin*\n\n19. **我们在文本摘要方面取得了哪些成就？** EMNLP 2020 [论文](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.33.pdf) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FSummarization\u002FHuang2020What.md)\n\n    *Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, Yue Zhang*\n\n#### [标注、分块、句法与解析](#content)\n\n1. **零样本跨语言语义解析的跨语言特征综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.10461) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FYang2019A.md)\n\n    *Jingfeng Yang, Federico Fancellu, Bonnie L. Webber*\n\n2. **基于成分结构和依存结构的句法-语义分析综述。** arXiv 2020 [论文](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.11056.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FZhang2020A.md)\n\n    *张美珊*\n\n3. **深度学习模型在序列标注领域的最新进展综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.06727) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FHe2020A.md)\n\n    *何志勇、王赞博、魏伟、冯珊珊、毛先凌、江盛*\n\n4. **语义解析综述。** AKBC 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.00978) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FKamath2019A.md)\n\n    *艾什瓦里娅·卡马斯、拉贾尔希·达斯*\n\n5. **从组合性视角看语义解析综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.14116.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FKumar2020A.md)\n\n    *帕万·库马尔、斯里坎塔·贝达图尔*\n\n6. **文本到SQL解析综述：概念、方法与未来方向。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.13629.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FQin2022A.md)\n\n    *秦博文、惠彬源、王立涵、杨敏、李金阳、李斌华、耿瑞英、曹荣宇、孙健、司洛、黄飞、李永斌*\n\n7. **上下文依赖的语义解析：综述。** COLING 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.00797.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FLi2020Context.md)\n\n    *李壮、屈丽珍、戈拉姆雷扎·哈法里*\n\n8. **神经网络序列标注中的设计挑战与误区。** COLING 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.04470) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FYang2018Design.md)\n\n    *杨杰、梁帅龙、张悦*\n\n9. **词性标注。** Wiley跨学科评论：计算统计学 2011 [论文](https:\u002F\u002Fwires.onlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1002\u002Fwics.195) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FMartinez2011Part‐of‐speech.md)\n\n    *安赫尔·R·马丁内斯*\n\n10. **语义元知识计算：语义元知识库应用与扩展的最新进展综述。** 计算机科学前沿 2021 [论文](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11704-020-0002-4) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FQi2021Sememe.md)\n\n    *齐凡超、谢若冰、臧远、刘志远、孙茂松*\n\n11. **句法分析综述。** 计算机与人文 1989 [论文](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF00058766) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FSanders1989Syntactic.md)\n\n    *奥尔顿·F·桑德斯和露丝·H·桑德斯*\n\n12. **词嵌入与神经网络中的句法表示——综述。** ITAT 2020 [论文](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.01063.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FLimisiewicz2020Syntax.md)\n\n    *托马什·利米谢维奇、大卫·马雷切克*\n\n13. **语义解析的鸿沟：自动数学文字题求解器综述。** IEEE模式分析与机器智能汇刊 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07290) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FTagging,-Chunking,-Syntax-and-Parsing\u002FZhang2020The.md)\n\n    *张东翔、王磊、张鲁明、戴炳天、沈恒涛*\n\n#### [文本分类](#content)\n\n1. **基于深度神经网络的文本分类主动学习综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.07267) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FSchröder2020A.md)\n\n    *克里斯托弗·施罗德、安德烈亚斯·尼克勒*\n\n2. **朴素贝叶斯机器学习在文本文档分类中的应用综述。** arXiv 2010 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1003.1795) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FVidhya2010A.md)\n\n    *K. A. Vidhya、G. Aghila*\n\n3. **文本分类中的数据增强综述。** ACM计算综述 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.03158) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FBayer2023A.md)\n\n    *马库斯·拜耳、马克-安德烈·考夫霍尔德、克里斯蒂安·罗伊特*\n\n4. **用于假新闻检测的自然语言处理综述。** LREC 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.00770) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FOshikawa2020A.md)\n\n    *雷·大石川、钱静、威廉·杨·王*\n\n5. **用于文本分类的短语结构学习方法综述。** arXiv 2014 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.5598) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FPrasad2014A.md)\n\n    *雷斯玛·普拉萨德、玛丽·普里亚·塞巴斯蒂安*\n\n6. **用于误传和虚假信息识别的姿态检测综述。** NAACL-HLT 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00242) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FHardalov2022A.md)\n\n    *莫姆奇尔·哈尔达洛夫、阿尔纳夫·阿罗拉、普雷斯拉夫·纳科夫、伊莎贝尔·奥根斯坦*\n\n7. **文本分类综述：从浅层学习到深度学习。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2008.00364.pdf) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FLi2020A.md)\n\n    *李倩、彭浩、李建新、夏聪颖、杨仁宇、孙立超、菲利普·S·余、何丽芳*\n\n8. **文本中的自动语言识别：综述。** 人工智能研究杂志 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08186) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FJauhiainen2019Automatic.md)\n\n    *汤米·雅乌希艾宁、马可·卢伊、马科斯·赞皮耶里、蒂莫西·鲍德温、克里斯特·林登*\n\n9. **基于深度学习的文本分类：全面综述。** ACM计算综述 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.03705) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FMinaee2022Deep.md)\n\n    *舍尔文·米纳伊、纳尔·卡尔布伦纳、埃里克·坎布里亚、纳尔杰斯·尼克扎德、梅萨姆·切纳格卢、高建峰*\n\n10. **利用姿态分类进行假新闻检测：综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.00181) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FLillie2019Fake.md)\n\n    *安德斯·埃德尔博·利利、埃米尔·雷夫斯加德·米德尔博厄*\n\n11. **文本分类中的分布外泛化：过去、现在与未来。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14104) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FYang2023Out-of-Distribution.md)\n\n    *杨琳怡、宋耀晓、任轩、吕晨阳、王一东、刘玲巧、王金东、詹妮弗·福斯特、张悦*\n\n12. **语义文本分类：过去与近期进展综述。** 信息处理管理 2018 [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0306457317305757) [文摘](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FAltinel2018Semantic.md)\n\n    *贝尔娜·阿尔蒂内尔、穆拉特·詹·加尼兹*\n\n13. **文本分类算法：综述。** Inf. 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.08067) [参考文献](\u002Fbib\u002FNatural-Language-Processing\u002FText-Classification\u002FKowsari2019Text.md)\n\n    *卡姆兰·科瓦里、基亚娜·贾法里·梅伊曼迪、莫杰塔巴·海达里萨法、桑贾娜·门杜、劳拉·E·巴恩斯、唐纳德·E·布朗*\n\n\n\n## 机器学习论文列表\n\n#### [架构](#content)\n\n1. **深度学习中注意力机制的通用综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.14263.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FBrauwers2022A.md)\n\n    *詹尼·布劳韦尔斯、弗拉维乌斯·弗拉辛卡尔*\n\n2. **关于更快更轻量级Transformer的实用综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.14636.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FFournier2021A.md)\n\n    *昆汀·富尔尼耶、盖坦·马索·卡隆、丹尼尔·阿洛伊斯*\n\n3. **二值化神经网络的回顾。** Electronics 2019 [论文](http:\u002F\u002Fwww.socolar.com\u002FArticle\u002FIndex?aid=100010075063&jid=100000022108) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FSimons2019A.md)\n\n    *泰勒·西蒙斯、戴杰·李*\n\n4. **深度学习中稀疏专家模型的回顾。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.01667.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FFedus2022A.md)\n\n    *威廉·费杜斯、杰夫·迪恩、巴雷特·佐普*\n\n5. **深度学习理论与架构的最新综述。** Electronics 2019 [论文](https:\u002F\u002Fwww.mdpi.com\u002F2079-9292\u002F8\u002F3\u002F292) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FAlom2019A.md)\n\n    *Md 扎汉吉尔·阿洛姆、塔雷克·M·塔哈、克里斯·雅科普西奇、斯特凡·韦斯特贝格、帕赫丁·西迪克、Mst 沙米玛·纳斯林、马赫穆杜尔·哈桑、布莱恩·C·范·埃森、阿卜杜勒·A·S·阿瓦尔以及维贾扬·K·阿萨里*\n\n6. **卷积神经网络的综述：分析、应用与展望。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.02806) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FLi2020A.md)\n\n    *泽文·李、温杰·杨、寿恒·彭、范·刘*\n\n7. **端到端自动驾驶的综述：架构与训练方法。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.06404) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FTampuu2020A.md)\n\n    *阿尔迪·坦普、马克西姆·塞米金、纳维德·穆罕默德、德米特罗·菲什曼、坦贝特·马蒂森*\n\n8. **深度卷积神经网络近期架构的综述。** Artif. Intell. Rev. 2020 [论文](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10462-020-09825-6) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FKhan2020A.md)\n\n    *阿西夫拉·汗、阿纳比亚·索海尔、乌梅·扎霍拉、阿克萨·赛义德·库雷希*\n\n9. **Transformer的综述。** AI Open 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.04554.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FLin2022A.md)\n\n    *田阳林、于欣王、向阳刘、锡鹏邱*\n\n10. **激活函数及其与Xavier和He正态初始化关系的综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.06632) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FDatta2020A.md)\n\n    *列昂尼德·达塔*\n\n11. **潜在树模型及其应用的综述。** J. Artif. Intell. Res. 2013 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1402.0577) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FMourad2013A.md)\n\n    *拉斐尔·穆拉德、克里斯汀·西诺凯、内文·连文张、滕飞刘、菲利普·勒雷*\n\n12. **现代可训练激活函数的综述。** Neural Networks 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.00817) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FApicella2021A.md)\n\n    *安德烈亚·阿皮切拉、弗朗切斯科·多纳鲁马、弗朗切斯科·伊斯格罗、罗伯托·普雷韦特*\n\n13. **视觉Transformer的综述。** IEEE Trans. Pattern Anal. Mach. Intell. 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.12556) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FHan2023A.md)\n\n    *凯·韩、云鹤王、汉廷陈、兴浩陈、建元郭、振华刘、叶辉唐、安晓、春景徐、艺星徐、兆辉杨、一满张、大成陶*\n\n14. **注意力模型的细致综述。** ACM Trans. Intell. Syst. Technol. 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.02874) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FChaudhari2021An.md)\n\n    *斯内哈·乔杜里、瓦伦·米塔尔、贡戈尔·波拉特坎、罗汉·拉马纳特*\n\n15. **自编码器简介。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.03898.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FMichelucci2022An.md)\n\n    *翁贝托·米凯鲁奇*\n\n16. **用于机器视觉的注意力机制与深度学习：现状综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.07550.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FHafiz2021Attention.md)\n\n    *阿卜杜勒·穆伊德·哈菲兹、沙比尔·艾哈迈德·帕拉、鲁夫·乌尔·阿拉姆·巴特*\n\n17. **计算机视觉中的注意力机制：综述。** Comput. Vis. Media 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.07624.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FGuo2022Attention.md)\n\n    *孟浩郭、天兴徐、江江刘、郑宁刘、鹏涛江、太江穆、宋海张、拉尔夫·R·马丁、明明程、诗敏胡*\n\n18. **大型网络：综述。** Comput. Sci. Rev. 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.03638) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FBedru2020Big.md)\n\n    *哈亚特·迪诺·贝德鲁、朔宇、新如肖、达张、梁天万、何国、冯夏*\n\n19. **二值化神经网络：综述。** Pattern Recognit. 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.03333) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FQin2020Binary.md)\n\n    *郝彤秦、瑞豪龚、翔龙刘、小白、京宽宋、尼库·塞贝*\n\n20. **深度回声状态网络（DeepESN）：简要综述。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.04323) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FGallicchio2017Deep.md)\n\n    *克劳迪奥·加利奇奥、阿莱西奥·米凯利*\n\n21. **深度树转换——简短综述。** INNSBDDL 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.01737) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FBacciu2019Deep.md)\n\n    *达维德·巴丘、安东尼奥·布鲁诺*\n\n22. **高效Transformer：综述。** ACM Comput. Surv. 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.06732) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FTay2023Efficient.md)\n\n    *易泰、莫斯塔法·德赫加尼、达拉·巴赫里、唐纳德·梅茨勒*\n\n23. **胶囊网络学习：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.02664.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FRibeiro2022Learning.md)\n\n    *法比奥·德·苏萨·里贝罗、凯文·杜阿尔特、迈尔斯·埃弗雷特、乔治奥斯·莱昂蒂迪斯、穆巴拉克·沙赫*\n\n24. **因果深度生成模型的机会：综述与未来方向。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.12351.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FZhou2023On.md)\n\n    *光林周、丽娜姚、熙伟徐、晨王、立明朱、坤张*\n\n25. **深度神经网络中的池化方法回顾。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07485) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FGholamalinezhad2020Pooling.md)\n\n    *侯赛因·戈拉马利内扎德、侯赛因·霍斯拉维*\n\n26. **Transformer 中的位置信息：综述。** 计算语言学 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.11090) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FDufter2022Position.md)\n\n    *菲利普·杜夫特、马丁·施密特、欣里希·舒策*\n\n27. **卷积神经网络的最新进展。** 模式识别 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.07108) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FGu2018Recent.md)\n\n    *顾九翔、王振华、杰森·库恩、马连洋、阿米尔·沙赫鲁迪、帅兵、刘婷、王兴兴、王刚、蔡建飞、陈土汉*\n\n28. **求和-乘积网络：综述。** arXiv 2020 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9363463) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FParís2020Sum-Product.md)\n\n    *伊亚戈·帕里斯、拉奎尔·桑切斯-考塞、弗朗西斯科·哈维尔·迪埃斯*\n\n29. **深度神经网络中的 Dropout 方法综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.13310) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FLabach2019Survey.md)\n\n    *亚历克斯·拉巴奇、霍贾特·萨莱希内贾德、沙赫罗赫·瓦莱伊*\n\n30. **基于注意力机制的 RNN 模型及其在计算机视觉中的应用综述。** arXiv 2016 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1601.06823) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FWang2016Survey.md)\n\n    *冯旺、戴维·M·J·塔克斯*\n\n31. **从 AlexNet 开始的历史：深度学习方法的全面综述。** arXiv 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01164) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FAlom2018The.md)\n\n    *穆罕默德·扎汉吉尔·阿洛姆、塔雷克·M·塔哈、克里斯托弗·雅科普西奇、斯特凡·韦斯特贝格、帕黑丁·西迪克、姆斯特·沙米玛·纳斯林、布赖恩·C·范·埃森、阿卜杜勒·A·S·阿瓦尔、维贾扬·K·阿萨里*\n\n32. **NLP 烹饪书：基于 Transformer 的深度学习架构的现代配方。** IEEE Access 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.10640.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FSingh2021The.md)\n\n    *苏尚特·辛格、奥西夫·马哈茂德*\n\n33. **视觉中的 Transformer：综述。** ACM 计算机科学评论 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.01169) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FKhan2022Transformers.md)\n\n    *萨尔曼·H·汗、穆扎米尔·纳西尔、穆纳瓦尔·哈亚特、赛义德·瓦卡斯·扎米尔、法哈德·沙赫巴兹·汗、穆巴拉克·沙赫*\n\n34. **理解 LSTM——长短期记忆循环神经网络教程。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.09586) [参考文献](\u002Fbib\u002FMachine-Learning\u002FArchitectures\u002FStaudemeyer2019Understanding.md)\n\n    *拉尔夫·C·斯陶德迈耶、埃里克·罗斯坦·莫里斯*\n\n#### [AutoML](#content)\n\n1. **神经架构搜索的全面综述：挑战与解决方案。** ACM 计算机科学评论 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.02903) [参考文献](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FRen2022A.md)\n\n    *任鹏珍、肖云、常晓军、黄宝耀、李志辉、陈晓江、王鑫*\n\n2. **推荐系统自动化机器学习的全面综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.01390) [参考文献](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FChen2022A.md)\n\n    *陈博、赵向宇、王叶静、范文琪、郭慧峰、唐瑞明*\n\n3. **硬件感知神经架构搜索的全面综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.09336) [参考文献](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FBenmeziane2021A.md)\n\n    *哈杰尔·本梅齐安、考塔尔·埃尔·马格劳伊、哈姆扎·欧阿尔努吉、斯迈勒·尼亚尔、马丁·维斯图巴、王乃刚*\n\n4. **用于深度神经网络架构搜索的元强化学习回顾。** arXiv 2018 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.07995.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FJaâfra2018A.md)\n\n    *耶斯米娜·贾阿夫拉、让·吕克·洛朗、阿琳·德鲁伊韦尔、穆罕默德·萨贝尔·纳塞尔*\n\n5. **神经架构搜索的综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.01392) [参考文献](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FWistuba2019A.md)\n\n    *马丁·维斯图巴、安布里什·拉瓦特、特贾斯维尼·佩达帕蒂*\n\n6. **图上的自动化机器学习：综述。** IJCAI 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00742) [参考文献](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FZhang2021Automated.md)\n\n    *张子威、王鑫、朱文武*\n\n7. **深度推荐系统的 AutoML：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.13922.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FZheng2022AutoML.md)\n\n    *郑睿琪、曲亮、崔斌、史玉辉、尹洪志*\n\n8. **AutoML：现状综述。** 知识驱动系统 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.00709) [参考文献](\u002Fbib\u002FMachine-Learning\u002FAutoML\u002FHe2021AutoML.md)\n\n    *何鑫、赵凯勇、楚晓文*\n\n9. **自动化机器学习框架的基准测试与综述。** 人工智能研究杂志 2021 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2015 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1211.4798) [参考文献](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FFoti2015A.md)\n\n    *尼古拉斯·J·福蒂、西奈德·A·威廉姆森*\n\n2. **贝叶斯深度学习的综述。** ACM 计算机科学评论 2021 [论文](http:\u002F\u002Farxiv.org\u002Fabs\u002F1604.01662) [参考文献](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FWang2021A.md)\n\n    *郝王、狄艳杨*\n\n3. **贝叶斯神经网络：简介与综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.12024) [参考文献](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FGoan2020Bayesian.md)\n\n    *伊森·戈恩、克林顿·福克斯*\n\n4. **贝叶斯非参数空间划分：综述。** IJCAI 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11394) [参考文献](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FFan2021Bayesian.md)\n\n    *许辉范、李彬、罗玲、斯科特·A·西森*\n\n5. **深度贝叶斯主动学习，近期进展简要综述。** arXiv 2020 [论文](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.08044.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FMohamadi2020Deep.md)\n\n    *萨尔曼·穆罕默迪、哈米德雷扎·阿敏达瓦尔*\n\n6. **动手实践贝叶斯神经网络——面向深度学习用户的教程。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.06823) [参考文献](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FJospin2020Hands-on.md)\n\n*洛朗·瓦伦丁·若斯潘、雷·L·邦廷、法里德·布萨伊德、哈米德·拉加、穆罕默德·本纳蒙*\n\n7. **将人类排除在闭环之外：贝叶斯优化综述。** IEEE 2016年会议论文 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=7352306) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FBayesian-Methods\u002FShahriari2016Taking.md)\n\n    *博巴克·沙赫里亚里、凯文·斯韦尔斯基、王子宇、瑞安·P·亚当斯、南多·德·弗雷塔斯*\n\n#### [分类、聚类与回归](#content)\n\n1. **持续学习综述：在分类任务中克服遗忘问题。** IEEE模式分析与机器智能汇刊 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.08383.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FLange2022A.md)\n\n    *马蒂亚斯·德·兰格、拉哈夫·阿尔朱迪、马克·马萨纳、萨拉·帕里索特、徐佳、阿莱斯·莱昂纳迪斯、格雷戈里·G·斯拉鲍、蒂内·图伊特拉尔斯*\n\n2. **大数据领域分类技术综述。** arXiv 2015年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1503.07477) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FKoturwar2015A.md)\n\n    *普拉富尔·科图尔瓦尔、希塔尔·吉拉塞、德巴焦蒂·穆克霍帕迪亚伊*\n\n3. **约束高斯过程回归综述：方法与实现挑战。** arXiv 2020年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09319) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FSwiler2020A.md)\n\n    *劳拉·P·斯威勒、马米孔·古利安、阿里·弗兰克尔、科斯敏·萨夫塔、约翰·D·杰克曼*\n\n4. **深度图聚类综述：分类体系、挑战与应用。** arXiv 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.12875.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FLiu2022A.md)\n\n    *刘悦、夏俊、周思航、王思伟、郭锡峰、杨西洪、梁科、涂文轩、李斯坦·Z、刘新旺*\n\n5. **面向Windows恶意软件分类的机器学习方法及挑战综述。** arXiv 2020年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09271) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FRaff2020A.md)\n\n    *爱德华·拉夫、查尔斯·尼古拉斯*\n\n6. **数据不平衡问题的分类与解决方法综述。** arXiv 2020年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11870) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FHasib2020A.md)\n\n    *汗·Md·哈西卜、Md·萨迪克·伊克巴尔、费萨尔·穆罕默德·沙阿、朱拜尔·阿尔·马赫穆德、马赫穆杜尔·哈桑·波佩尔、Md·伊姆兰·侯赛因·绍罗夫、沙基尔·艾哈迈德、奥拜杜尔·拉赫曼*\n\n7. **所有分类器均可从深度网络中学习的技术综述：模型、优化与正则化。** arXiv 2019年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.04791.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FGhods2019A.md)\n\n    *阿里雷扎·戈兹德斯、黛安·J·库克*\n\n8. **多视图聚类综述。** arXiv 2017年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.06246) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FChao2017A.md)\n\n    *赵国清、孙世亮、毕金波*\n\n9. **多标签分类方法的全面比较研究。** Expert Syst. Appl. 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07113) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FBogatinovski2022Comprehensive.md)\n\n    *贾斯敏·博加蒂诺夫斯基、柳普乔·托多罗夫斯基、萨索·杰罗斯基、德拉吉·科切夫*\n\n10. **深度聚类：全面综述。** arXiv 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.04142.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FRen2022Deep.md)\n\n    *任亚州、蒲静宇、杨志猛、许杰、李国锋、蒲晓蓉、菲利普·S·余、何丽芳*\n\n11. **时间序列分类中的深度学习：综述。** Data Min. Knowl. Discov. 2019年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04356) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FFawaz2019Deep.md)\n\n    *哈桑·伊斯梅尔·法瓦兹、热尔曼·福雷斯蒂耶、乔纳森·韦伯、拉哈桑·伊杜姆加尔、皮埃尔-阿兰·穆勒*\n\n12. **你的分类问题有多复杂？关于分类复杂度测量的综述。** arXiv 2018年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.03591) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FClassification,-Clustering-and-Regression\u002FLorena2018How.md)\n\n    *安娜·卡罗琳娜·洛雷纳、路易斯·保罗·F·加西亚、延斯·莱曼、马西里奥·C·P·德·索托、田金浩*\n\n#### [计算机视觉](#content)\n\n1. **3D人体运动预测：综述。** Neurocomputing 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.01593) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FLyu20223D.md)\n\n    *吕克迪、陈海鹏、刘振光、张贝琪、王瑞莉*\n\n2. **自动驾驶中的3D目标检测：综述。** Pattern Recognit. 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.10823.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FQian20223D.md)\n\n    *钱锐、赖鑫、李希荣*\n\n3. **面向自动驾驶的基于图像的3D目标检测：综述。** arXiv 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.02980.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FMa20223D.md)\n\n    *马新竹、欧阳万力、安德烈娅·西蒙内利、埃丽莎·里奇*\n\n4. **基于Transformer的3D视觉：综述。** arXiv 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.04309.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FLahoud20223D.md)\n\n    *让·拉胡德、曹嘉乐、法哈德·沙赫巴兹·汗、希沙姆·乔拉卡尔、拉奥·穆罕默德·安瓦尔、萨尔曼·汗、杨明轩*\n\n5. **基于深度学习的图像分类任务中自动化数据增强算法综述。** arXiv 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.06544.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FYang2022A.md)\n\n    *杨子涵、理查德·O·辛诺特、詹姆斯·贝利、邱宏科*\n\n6. **针对计算机视觉模型的黑盒对抗攻击综述。** arXiv 2019年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.01667.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FBhambri2019A.md)\n\n    *西丹特·班布里、苏马纽·穆库、阿维纳什·图拉西、阿伦·巴拉吉·布杜鲁*\n\n7. **深度面部修复综述：去噪、超分辨率、去模糊、去除伪影。** arXiv 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.02831) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FWang2022A.md)\n\n    *王涛、张凯豪、陈玄熙、罗文翰、邓建康、卢彤、曹晓春、刘伟、李洪东、斯特凡诺斯·扎菲里乌*\n\n8. **语义分割损失函数综述。** CIBCB 2020年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.14822.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FJadon2020A.md)\n\n    *舒鲁蒂·贾登*\n\n9. **现代基于深度学习的目标检测模型综述。** Digit. Signal Process. 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.11892.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZaidi2022A.md)\n\n    *赛义德·萨希尔·阿巴斯·扎伊迪、穆罕默德·萨马尔·安萨里、阿斯拉·阿斯拉姆、纳迪娅·坎瓦尔、马穆娜·纳维德·阿斯加尔、布莱恩·李*\n\n10. **人体姿态估计自上而下方法综述。** arXiv 2022年 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.02656.pdf) [BibTeX](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FNguyen2022A.md)\n\n    *阮通贵、米兰·克雷索维奇*\n\n11. **视觉-语言预训练模型综述。** IJCAI 2022 [论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0762.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FDu2022A.md)\n\n    *Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao*\n\n12. **视觉感官异常检测综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.07006.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FJiang2022A.md)\n\n    *Xi Jiang, Guoyang Xie, Jinbao Wang, Yong Liu, Chengjie Wang, Feng Zheng, Yaochu Jin*\n\n13. **视觉Transformer综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.06091.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FLiu2021A.md)\n\n    *Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao Shi, Jianping Fan, Zhiqiang He*\n\n14. **增强现实、混合现实和虚拟现实在自然与环境领域的应用综述。** HCI 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.12024) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FRambach2021A.md)\n\n    *Jason R. Rambach, Gergana Lilligreen, Alexander Schäfer, Ramya Bankanal, Alexander Wiebel, Didier Stricker*\n\n15. **用于图像检索的深度哈希综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.05627) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZhang2020A.md)\n\n    *Xiaopeng Zhang*\n\n16. **医学图像分析中的深度学习综述。** Medical Image Anal. 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.05747) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FLitjens2017A.md)\n\n    *Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W. M. van der Laak, Bram van Ginneken, Clara I. Sánchez*\n\n17. **视频分割中深度学习技术的综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.01153.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FWang2021A.md)\n\n    *Wenguan Wang, Tianfei Zhou, Fatih Porikli, David J. 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Jay Kuo*\n\n29. **基于重建的组合场景表征学习：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.07135.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FYuan2022Compositional.md)\n\n    *Jinyang Yuan, Tonglin Chen, Bin Li, Xiangyang Xue*\n\n30. **从极稀疏数据中进行深度补全：综述。** IEEE Trans. Pattern Anal. Mach. Intell. 2022 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9984942) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FHu2022Deep.md)\n\n    *Junjie Hu, Chenyu Bao, Mete Ozay, Chenyou Fan, Qing Gao, Honghai Liu, Tin Lun Lam*\n\n31. **深度图像去模糊：综述。** Int. J. Comput. 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Hoi*\n\n35. **基于深度学习的实例检索：综述。** IEEE模式分析与机器智能汇刊 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.11282.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FChen2021Deep.md)\n\n    *陈伟、刘宇、王卫平、埃尔温·巴克尔、西奥多罗斯·乔治乌、保罗·菲古斯、刘莉、迈克尔·S·刘易*\n\n36. **基于深度学习的场景分类：综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.10531) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZeng2021Deep.md)\n\n    *曾德鲁、廖敏宇、穆罕默德·塔瓦科利安、郭玉兰、周博磊、胡德文、马蒂·皮耶蒂凯宁、刘莉*\n\n37. **用于人体解析的深度学习技术：综述与展望。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.00394.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FYang2023Deep.md)\n\n    *杨璐、贾文赫、李珊、宋青*\n\n38. **高效高分辨率深度学习：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.13050.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FBakhtiarnia2022Efficient.md)\n\n    *阿里安·巴赫蒂亚尔尼亚、张琪、亚历山德罗斯·伊奥西菲迪斯*\n\n39. **基于几何与学习的网格去噪：全面综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.00841.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FChen2022Geometric.md)\n\n    *陈洪华、魏明强、王军*\n\n40. **基于深度学习的图像分割：综述。** IEEE模式分析与机器智能汇刊 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2001.05566.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FMinaee2022Image.md)\n\n    *谢尔文·米纳伊、尤里·博伊科夫、法蒂赫·波里克利、安东尼奥·普拉萨、纳赛尔·凯塔纳瓦兹、德米特里·特尔佐波洛斯*\n\n41. **基于深度学习的图像\u002F视频异常检测：综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.01739) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FMohammadi2021Image.md)\n\n    *巴赫拉姆·穆罕默迪、马赫穆德·法蒂、穆罕默德·萨博克鲁*\n\n42. **图像到图像的转换：方法与应用。** IEEE多媒体汇刊 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08629) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FPang2022Image-to-Image.md)\n\n    *庞英雪、林建新、秦涛、陈志博*\n\n43. **目标检测中的类别不平衡问题：综述。** IEEE模式分析与机器智能汇刊 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.00169) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FOksuz2021Imbalance.md)\n\n    *凯末尔·奥克苏兹、巴里斯·坎·坎、希南·卡尔坎、埃姆雷·阿克巴斯*\n\n44. **用于自动驾驶目标检测的毫米波雷达与视觉融合：综述。** Sensors 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.03004.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FWei2022MmWave.md)\n\n    *魏志清、张峰凯、常硕、刘阳阳、吴慧慈、冯志勇*\n\n45. **面向自动驾驶感知的多模态传感器融合：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.02703.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FHuang2022Multi-modal.md)\n\n    *黄可丽、史博天、李翔、李欣、黄思远、李益康*\n\n46. **过去二十年的目标检测：综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.05055) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZou2019Object.md)\n\n    *邹正霞、石振威、郭宇红、叶杰平*\n\n47. **视觉Transformer的最新进展：综述及未来工作展望。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01536.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FIslam2022Recent.md)\n\n    *卡瓦尔·伊斯兰*\n\n48. **从单目图像中恢复三维人体网格：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01923.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FTian2022Recovering.md)\n\n    *田雅婷、张洪文、刘业斌、王立民*\n\n49. **单幅图像超分辨率方法：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.11763.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FMaral2022Single.md)\n\n    *巴哈丁·坎·马拉尔*\n\n50. **视频中的时序句子定位：综述与未来方向。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.08071.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FZhang2022Temporal.md)\n\n    *张浩、孙爱欣、景伟、周天一*\n\n51. **端到端深度人脸识别的要素：最新进展综述。** ACM计算综述 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.13290.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FDu2022The.md)\n\n    *杜航、施海琳、曾丹、张晓平、梅涛*\n\n52. **机器学习对影像引导介入治疗中二维\u002F三维配准的影响：系统综述与展望。** Frontiers Robotics AI 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.02238.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FUnberath2021The.md)\n\n    *马蒂亚斯·昂贝拉特、高聪、胡义成、马克·朱迪什、罗素·H·泰勒、梅赫兰·阿曼德、罗伯特·B·格鲁普*\n\n53. **海龟保护的需求与现状，以及相关计算机视觉技术进展综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.14061.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FPaul2021The.md)\n\n    *阿迪提亚·乔蒂·保罗*\n\n54. **遥感中的Transformer：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.01206.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FAleissaee2022Transformers.md)\n\n    *阿卜杜拉齐兹·阿梅尔·阿莱萨伊、阿曼迪普·库马尔、拉奥·穆罕默德·安韦尔、萨尔曼·汗、希沙姆·乔拉卡尔、夏圭松、法哈德·沙赫巴兹·汗*\n\n55. **Transformer与视觉学习理解：综合评述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12944.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FYang2022Transformers.md)\n\n    *杨雨婷、焦立成、刘旭、刘芳、杨书源、冯志熙、唐旭*\n\n56. **基于深度学习的视频无监督域适应：综合综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10412.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FComputer-Vision\u002FXu2022Video.md)\n\n    *徐跃聪、曹浩志、陈正华、李晓丽、谢丽华、杨建飞*\n\n#### [对比学习](#content)\n\n1. **关于对比自监督学习的综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.00362) [引用](\u002Fbib\u002FMachine-Learning\u002FContrastive-Learning\u002FJaiswal2020A.md)\n\n    *阿希什·贾伊斯瓦尔、阿什温·拉梅什·巴布、穆罕默德·扎基·扎德、黛芭普里亚·班纳吉、菲利亚·马凯东*\n\n2. **对比表示学习：框架与综述。** IEEE Access 2020 [论文](http:\u002F\u002Fdoras.dcu.ie\u002F25121\u002F1\u002FACCESS3031549.pdf) [引用](\u002Fbib\u002FMachine-Learning\u002FContrastive-Learning\u002FLe-Khac2020Contrastive.md)\n\n    *福克·H·勒-喀克、格雷厄姆·希利、艾伦·F·斯米顿*\n\n3. **自监督学习：生成式还是对比式？** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08218) [引用](\u002Fbib\u002FMachine-Learning\u002FContrastive-Learning\u002FLiu2020Self-supervised.md)\n\n    *肖刘、张凡锦、侯振宇、王兆宇、李勉、张静、唐杰*\n\n#### [课程学习](#content)\n\n1. **课程学习综述。** IEEE模式分析与机器智能汇刊 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.13166) [bib](\u002Fbib\u002FMachine-Learning\u002FCurriculum-Learning\u002FWang2022A.md)\n\n    *Xin Wang, Yudong Chen, Wenwu Zhu*\n\n2. **深度强化学习中的自动课程学习：简短综述。** IJCAI 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.04664) [bib](\u002Fbib\u002FMachine-Learning\u002FCurriculum-Learning\u002FPortelas2020Automatic.md)\n\n    *Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, Pierre-Yves Oudeyer*\n\n3. **强化学习领域的课程学习：框架与综述。** 机器学习研究期刊 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.04960) [bib](\u002Fbib\u002FMachine-Learning\u002FCurriculum-Learning\u002FNarvekar2020Curriculum.md)\n\n    *Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone*\n\n4. **课程学习：综述。** 国际计算机视觉杂志 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.10382) [bib](\u002Fbib\u002FMachine-Learning\u002FCurriculum-Learning\u002FSoviany2022Curriculum.md)\n\n    *Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe*\n\n#### [数据增强](#content)\n\n1. **数据集蒸馏的全面综述。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.05603) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FLei2023A.md)\n\n    *Shiye Lei, Dacheng Tao*\n\n2. **深度学习中图像增强技术的全面综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.01491.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FXu2022A.md)\n\n    *Mingle Xu, Sook Yoon, Alvaro Fuentes, Dong Sun Park*\n\n3. **基于深度学习的图像分类任务中自动化数据增强算法的综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.06544.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FYang2022A.md)\n\n    *Zihan Yang, Richard O. Sinnott, James Bailey, Qiuhong Ke*\n\n4. **自然语言处理中数据增强方法的综述。** ACL 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.03075.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FFeng2021A.md)\n\n    *Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard H. Hovy*\n\n5. **基于混合的数据增强综述：分类、方法、应用及可解释性。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.10888.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FCao2022A.md)\n\n    *Chengtai Cao, Fan Zhou, Yurou Dai, Jianping Wang*\n\n6. **深度学习中图像数据增强的综述。** 大数据期刊 2019 [论文](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1186\u002Fs40537-019-0197-0) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FShorten2019A.md)\n\n    *Connor Shorten, Taghi M. Khoshgoftaar*\n\n7. **神经网络用于时间序列分类时数据增强的实证研究。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.15951) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FIwana2020An.md)\n\n    *Brian Kenji Iwana, Seiichi Uchida*\n\n8. **自然语言处理中的数据增强方法：综述。** AI Open 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.01852.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FLi2022Data.md)\n\n    *Bohan Li, Yutai Hou, Wanxiang Che*\n\n9. **图上的数据增强：技术综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.09970.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FZhou2022Data.md)\n\n    *Jiajun Zhou, Chenxuan Xie, Zhenyu Wen, Xiangyu Zhao, Qi Xuan*\n\n10. **数据蒸馏：综述。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.04272.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FSachdeva2023Data.md)\n\n    *Noveen Sachdeva, Julian J. McAuley*\n\n11. **数据集蒸馏：全面回顾。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.07014.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FYu2023Dataset.md)\n\n    *Ruonan Yu, Songhua Liu, Xinchao Wang*\n\n12. **深度学习中时间序列数据增强：综述。** IJCAI 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.12478) [bib](\u002Fbib\u002FMachine-Learning\u002FData-Augmentation\u002FWen2021Time.md)\n\n    *Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, Huan Xu*\n\n#### [深度学习通用方法](#content)\n\n1. **不同变量范式下深度学习因果发现的回顾与路线图。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.06367) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FChen2022A.md)\n\n    *Hang Chen, Keqing Du, Xinyu Yang, Chenguang Li*\n\n2. **深度主动学习综述。** ACM计算综述 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.00236) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FRen2022A.md)\n\n    *Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, Xin Wang*\n\n3. **边缘网络中用于数据缓存的深度学习综述。** Informatics 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.07235) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FWang2020A.md)\n\n    *Yantong Wang, Vasilis Friderikos*\n\n4. **数学推理中的深度学习综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.10535.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FLu2022A.md)\n\n    *Pan Lu, Liang Qiu, Wenhao Yu, Sean Welleck, Kai-Wei Chang*\n\n5. **科学发现中的深度学习综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.11755.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FRaghu2020A.md)\n\n    *Maithra Raghu, Eric Schmidt*\n\n6. **标签噪声表示学习的过去、现在与未来：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.04406.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FHan2020A.md)\n\n    *Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama*\n\n7. **硬件中的类脑计算与神经网络综述。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.06963) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FSchuman2017A.md)\n\n    *Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, James S. Plank*\n\n8. **深度神经网络中的不确定性综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.03342.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Learning-General-Methods\u002FGawlikowski2021A.md)\n\n    *Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna M. 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Mach. Learn. 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Intell. 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06877) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FArora2021A.md)\n\n*Saurabh Arora, Prashant Doshi*\n\n8. **面向动态变化环境的强化学习算法综述。** ACM 计算综述 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.10619) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FPadakandla2022A.md)\n\n    *Sindhu Padakandla*\n\n9. **基于自然语言的强化学习综述。** IJCAI 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.03926) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLuketina2019A.md)\n\n    *Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel*\n\n10. **强化学习技术综述：策略、最新进展与未来方向。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.06921) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMondal2020A.md)\n\n    *Amit Kumar Mondal*\n\n11. **深度强化学习中的零样本泛化综述。** 人工智能研究杂志 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.09794.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FKirk2023A.md)\n\n    *Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel*\n\n12. **面向音频应用的深度强化学习综述。** 人工智能评论 2023 [论文](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.00240.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLatif2023A.md)\n\n    *Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria*\n\n13. **面向数据处理与分析的深度强化学习综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.04526.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FCai2021A.md)\n\n    *Qingpeng Cai, Can Cui, Yiyuan Xiong, Wei Wang, Zhongle Xie, Meihui Zhang*\n\n14. **可解释强化学习综述：概念、算法与挑战。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.06665.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FQing2022A.md)\n\n    *Yunpeng Qing, Shunyu Liu, Jie Song, Mingli Song*\n\n15. **强化学习中的内在动机综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.06976) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FAubret2019A.md)\n\n    *Arthur Aubret, Laëtitia Matignon, Salima Hassas*\n\n16. **面向组合优化的强化学习综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2008.12248.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FYang2020A.md)\n\n    *Yunhao Yang, Andrew B. Whinston*\n\n17. **面向推荐系统的强化学习综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.10665.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLin2021A.md)\n\n    *Yuanguo Lin, Yong Liu, Fan Lin, Pengcheng Wu, Wenhua Zeng, Chunyan Miao*\n\n18. **通过在真实机器人上评估深度强化学习算法来提升可重复性：一项综述。** CoRL 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.03772) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLynnerup2019A.md)\n\n    *Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam*\n\n19. **强化学习中的Transformer综述。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.03044.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLi2023A.md)\n\n    *Wenzhe Li, Hao Luo, Zichuan Lin, Chongjie Zhang, Zongqing Lu, Deheng Ye*\n\n20. **通过内在奖励调整行为：综述与实证研究。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.07865.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLinke2019Adapting.md)\n\n    *Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White*\n\n21. **自动化强化学习（AutoRL）：综述与开放问题。** 人工智能研究杂志 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.03916) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FParker-Holder2022Automated.md)\n\n    *Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer*\n\n22. **经济学领域中深度强化学习方法及应用的综合评述。** arXiv 2020 [论文](https:\u002F\u002Fwww.mdpi.com\u002F2227-7390\u002F8\u002F10\u002F1640) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMosavi2020Comprehensive.md)\n\n    *Amir Mosavi, Pedram Ghamisi, Yaser Faghan, Puhong Duan*\n\n23. **强化学习领域的课程学习：框架与综述。** 机器学习研究期刊 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.04960.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FNarvekar2020Curriculum.md)\n\n    *Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone*\n\n24. **面向高维问题的基于模型的深度强化学习综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.05598) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FPlaat2020Deep.md)\n\n    *Aske Plaat, Walter Kosters, Mike Preuss*\n\n25. **面向自动驾驶的深度强化学习综述。** IEEE 智能交通系统汇刊 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.00444.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FKiran2022Deep.md)\n\n    *B. Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A. Al Sallab, Senthil Kumar Yogamani, Patrick Pérez*\n\n26. **面向临床决策支持的深度强化学习：简要综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.09475.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLiu2019Deep.md)\n\n    *Siqi Liu, Kee Yuan Ngiam, Mengling Feng*\n\n27. **面向物流与运输系统中需求驱动型服务的深度强化学习综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.04462.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FZong2021Deep.md)\n\n    *Zefang Zong, Tao Feng, Tong Xia, Depeng Jin, Yong Li*\n\n28. **面向智能交通系统的深度强化学习综述。** IEEE 智能交通系统汇刊 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.00935.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FHaydari2022Deep.md)\n\n    *Ammar Haydari, Yasin Yilmaz*\n\n29. **量化算法交易中的深度强化学习：综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.00123.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FPricope2021Deep.md)\n\n    *Tidor-Vlad Pricope*\n\n30. **深度强化学习：简要综述。** IEEE 信号处理杂志 2017 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8103164) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FArulkumaran2017Deep.md)\n\n    *Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath*\n\n31. **深度强化学习：概述。** arXiv 2017 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.07274) [参考文献](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FLi2017Deep.md)\n\n    *Yuxi Li*\n\n32. **无导数强化学习：综述。** 前沿计算机科学 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.05710) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FQian2021Derivative-Free.md)\n\n    *钱洪，于洋*\n\n33. **面向广义XAI的可解释强化学习：概念框架与综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.09003.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FDazeley2021Explainable.md)\n\n    *理查德·戴兹利，彼得·范普勒，弗朗西斯科·克鲁斯*\n\n34. **基于特征的聚合与深度强化学习：综述及若干新实现。** IEEE中国自动化学会期刊 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04577) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FBertsekas2019Feature-Based.md)\n\n    *迪米特里·P·贝尔塞卡斯*\n\n35. **基于模型的强化学习：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.16712) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMoerland2020Model-based.md)\n\n    *托马斯·M·莫兰德，约斯特·布罗肯斯，卡索琳·M·容克*\n\n36. **用于组合优化的强化学习：综述。** 计算机运筹学 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.03600) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FMazyavkina2021Reinforcement.md)\n\n    *尼娜·马扎维金娜，谢尔盖·斯维里多夫，谢尔盖·伊万诺夫，叶夫根尼·布尔纳耶夫*\n\n37. **医疗健康领域的强化学习：综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.08796.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FYu2019Reinforcement.md)\n\n    *于超，刘继明，沙米尔·内马蒂*\n\n38. **机器人领域深度强化学习中的模拟到现实迁移：综述。** SSCI 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.13303.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FZhao2020Sim-to-Real.md)\n\n    *赵文帅，豪尔赫·佩尼亚·奎拉尔塔，托米·韦斯特伦德*\n\n39. **语言处理中强化学习的综述。** 人工智能评论 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.05565) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FUc-Cetina2023Survey.md)\n\n    *维克托·乌克-塞蒂纳，尼古拉斯·纳瓦罗-格雷罗，阿纳贝尔·马丁-冈萨雷斯，科尔内利乌斯·韦伯，施特凡·韦尔姆特*\n\n40. **深度强化学习中概率图模型与变分推断的教程与综述。** SSCI 2019 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.09381.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDeep-Reinforcement-Learning\u002FSun2019Tutorial.md)\n\n    *孙旭东，伯恩德·比施尔*\n\n#### [扩散模型](#content)\n\n1. **生成式扩散模型综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.02646) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FCao2022A.md)\n\n    *曹汉群，陈成，高张阳，陈光勇，彭安恒，李世忠*\n\n2. **医学图像分析中的扩散模型：综合综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.07804) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FKazerouni2022Diffusion.md)\n\n    *阿米尔侯赛因·卡泽鲁尼，埃桑·霍达帕纳·阿格达姆，莫因·海达里，雷扎·阿扎德，穆赫森·法亚兹，伊尔克尔·哈奇哈利洛卢，多丽特·梅尔霍夫*\n\n3. **NLP中的扩散模型：综述。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14671) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FZhu2023Diffusion.md)\n\n    **\n\n4. **视觉领域的扩散模型：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.04747.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FCroitoru2022Diffusion.md)\n\n    *弗洛里内尔-阿林·克罗伊托鲁，弗拉德·洪德鲁，拉杜·图多尔·伊奥内斯库，穆巴拉克·沙赫*\n\n5. **扩散模型：方法与应用的综合综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.00796.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FYang2022Diffusion.md)\n\n    *杨凌，张志龙，宋扬，洪申达，徐润生，赵悦，邵英霞，张文涛，杨明轩，崔斌*\n\n6. **视觉领域的高效扩散模型：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.09292.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FDiffusion-Models\u002FUlhaq2022Efficient.md)\n\n    *安瓦尔·乌尔哈克，纳维德·阿赫塔尔，加娜·波格列布娜*\n\n#### [联邦学习](#content)\n\n1. **联邦学习系统综述：数据隐私与保护的愿景、炒作与现实。** arXiv 2019 [论文](http:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.09693.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FLi2019A.md)\n\n    *李钦彬，温泽毅，吴兆敏，胡思旭，王乃博，刘旭，何炳胜*\n\n2. **异构联邦学习综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.04505.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FGao2022A.md)\n\n    *高大山，姚欣，杨强*\n\n3. **联邦学习系统中的安全与隐私保障：综述、研究挑战与未来方向。** 工程应用人工智能 2021 [论文](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.06810.pdf) 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M. 斯瓦尔纳·普里亚、国越·范、卡帕尔·德夫、普拉文·库马尔·雷迪·马迪昆塔、蒂帕·雷迪·加德卡卢、天·辉-特*\n\n8. **联邦学习中的隐私与鲁棒性：攻击与防御。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06337) [参考文献](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FLyu2020Privacy.md)\n\n    *吕凌娟、于瀚、马兴军、孙立超、赵俊、杨强、Philip S. Yu*\n\n9. **联邦学习的威胁：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.02133) [参考文献](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FLyu2020Threats.md)\n\n    *吕凌娟、于瀚、杨强*\n\n10. **在联邦学习中利用无标签数据：综述与展望。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11545) [参考文献](\u002Fbib\u002FMachine-Learning\u002FFederated-Learning\u002FJin2020Towards.md)\n\n    *金一伦、魏锡光、刘洋、杨强*\n\n#### [少样本与零样本学习](#content)\n\n1. **少样本学习的全面综述：发展、应用、挑战与机遇。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.06743.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FFew-Shot-and-Zero-Shot-Learning\u002FSong2022A.md)\n\n    *宋义生、王婷、蒙达尔、萨胡*\n\n2. **深度强化学习中零样本泛化的综述。** J. Artif. Intell. 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Knowl. Discov. 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[文摘](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FSchölkopf2021Towards.md)\n\n    *伯恩哈德·舍尔科普夫、弗朗切斯科·洛卡泰洛、斯特凡·鲍尔、南·罗斯玛丽·凯、纳尔·卡尔赫布伦纳、阿尼鲁德·戈亚尔、约书亚·本吉奥*\n\n53. **迈向分布外泛化：综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.13624.pdf) [文摘](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FShen2021Towards.md)\n\n    *沈哲彦、刘嘉硕、何悦、张星轩、许仁哲、于涵、崔鹏*\n\n54. **机器学习、自主系统和神经网络验证综述。** arXiv 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.01989) [文摘](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FXiang2018Verification.md)\n\n    *向伟明、帕特里克·穆绍、阿雅娜·A·怀尔德、迭戈·曼萨纳斯·洛佩斯、纳撒尼尔·汉密尔顿、杨晓东、乔尔·A·罗森菲尔德、泰勒·T·约翰逊*\n\n55. **知识能为机器学习带来什么？——面向结构化数据的小样本学习综述。** ACM 智能系统与技术汇刊 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.06410) [文摘](\u002Fbib\u002FMachine-Learning\u002FGeneral-Machine-Learning\u002FHu2022What.md)\n\n    *胡洋、艾德里安·查普曼、温桂华、温迪·霍尔*\n\n#### 【生成对抗网络】 (#content)\n\n1. **生成对抗网络综述：算法、理论与应用。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.06937) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FGui2020A.md)\n\n    *桂杰、孙振楠、温永刚、陶大成、叶杰平*\n\n2. **深度图生成综述：方法与应用。** LoG 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.06714.pdf) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FZhu2022A.md)\n\n    *朱燕桥、杜元琪、王银凯、徐一辰、张洁宇、刘强、吴树*\n\n3. **生成对抗网络综述：变体、应用与训练。** ACM 计算机科学评论 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.05132) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FJabbar2022A.md)\n\n    *阿卜杜勒·贾巴尔、李希、布拉赫拉·奥马尔*\n\n4. **数据合成与对抗网络：癌症影像中的综述与荟萃分析。** 医学图像分析 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.09543) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FOsuala2023Data.md)\n\n    *理查德·奥苏阿拉、凯萨尔·库希巴尔、莉迪娅·加鲁乔、阿基斯·利纳尔多斯、祖赞娜·沙夫拉诺夫斯卡、施特凡·克莱因、本·格洛克尔、奥利弗·迪亚斯、卡里姆·莱卡迪尔*\n\n5. **深度生成建模：VAE、GAN、归一化流、基于能量的模型及自回归模型的比较研究。** IEEE 模式分析与机器智能汇刊 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.04922) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FBond-Taylor2022Deep.md)\n\n    *萨姆·邦德-泰勒、亚当·利奇、杨龙、克里斯·G·威尔科克斯*\n\n6. **三维表示上的深度生成模型：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.15663.pdf) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FShi2022Deep.md)\n\n    *石子凡、彭思达、许英浩、廖怡怡、申宇俊*\n\n7. **GAN计算机能创作艺术吗？利用生成对抗网络进行视觉艺术、音乐和文学文本生成的综述。** Displays 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.03857.pdf) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FShahriar2022GAN.md)\n\n    *萨基布·沙里亚尔*\n\n8. **GAN反演：综述。** IEEE 模式分析与机器智能汇刊 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.05278.pdf) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FXia2023GAN.md)\n\n    *夏伟豪、张玉伦、杨宇久、薛景浩、周博磊、杨明轩*\n\n9. **计算机视觉中的生成对抗网络：综述与分类体系。** ACM 计算机科学评论 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01529) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FWang2022Generative.md)\n\n    *王正威、谢琦、托马斯·E·沃德*\n\n10. **生成对抗网络在人类情感合成中的应用：综述。** IEEE Access 2020 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9279199) [文摘](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FHajarolasvadi2020Generative.md)\n\n    *努辛·哈贾罗拉斯瓦迪、米格尔·阿琼纳·拉米雷斯、韦斯利·贝卡罗、哈桑·德米雷尔*\n\n11. **生成对抗网络：面向隐私与安全应用的综述。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.03785) [bib](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FCai2021Generative.md)\n\n    *蔡志鹏、熊作斌、徐洪辉、王鹏、李伟、潘毅*\n\n12. **生成对抗网络：概述。** IEEE Signal Process. Mag. 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.07035) [bib](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FCreswell2018Generative.md)\n\n    *安东尼娅·克雷斯韦尔、汤姆·怀特、文森特·杜穆兰、凯·阿鲁库马兰、比斯瓦·森古普塔、阿尼尔·A·巴拉思*\n\n13. **生成对抗网络及其变体的工作原理：综述。** ACM Comput. Surv. 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05914) [bib](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FHong2019How.md)\n\n    *洪勇俊、黄宇元、柳在润、尹成浩*\n\n14. **生成对抗网络的稳定性：综述。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1910.00927) [bib](\u002Fbib\u002FMachine-Learning\u002FGenerative-Adversarial-Networks\u002FWiatrak2019Stabilizing.md)\n\n    *马切伊·维亚特拉克、斯特凡诺·V·阿尔布雷希特、安德鲁·尼斯特罗姆*\n\n#### [图神经网络](#content)\n\n1. **图嵌入的全面综述：问题、技术与应用。** IEEE Trans. Knowl. Data Eng. 2018 [论文](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8294302) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FCai2018A.md)\n\n    *蔡鸿云、文森特·W·郑、凯文·陈传昌*\n\n2. **图级别学习的全面综述。** arXiv 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.05860.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FYang2023A.md)\n\n    *杨振宇、张戈、吴佳、杨健、沈泉祖、薛珊、周川、阿加瓦尔、彭浩、胡文斌、汉考克、利奥*\n\n3. **关于图神经网络的全面综述。** IEEE Trans. Neural Networks Learn. Syst. 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.00596) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FWu2021A.md)\n\n    *吴宗翰、潘世睿、陈凤文、龙国栋、张承启、余Philip S.*\n\n4. **关于可信图神经网络的全面综述：隐私、鲁棒性、公平性和可解释性。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.08570.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FDai2022A.md)\n\n    *戴恩燕、赵天翔、朱怀胜、徐俊杰、郭志明、刘辉、唐继良、王苏航*\n\n5. **可解释图神经网络的综述：分类体系与评估指标。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.12599) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FLi2022A.md)\n\n    *李义桥、周建龙、维尔玛、陈芳*\n\n6. **计算机视觉中图神经网络与图变换器的综述：任务导向视角。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.13232.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FChen2022A.md)\n\n    *陈超奇、吴雨霜、戴琪源、周宏宇、许牧田、杨思贝、韩晓光、于一舟*\n\n7. **用于知识图谱补全的图神经网络综述。** arXiv 2020 [论文](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.12374.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FArora2020A.md)\n\n    *西丹特·阿罗拉*\n\n8. **图结构学习的综述：进展与机遇。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03036) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FZhu2021A.md)\n\n    *朱艳巧、徐伟志、张景浩、杜元奇、张洁宇、刘强、杨卡尔、吴树*\n\n9. **异构图嵌入的综述：方法、技术、应用及数据来源。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.14867.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FWang2020A.md)\n\n    *王晓、薄德宇、石川、范绍华、叶燕芳、余Philip S.*\n\n10. **图神经网络表达能力的综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.04078) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FSato2020A.md)\n\n    *佐藤龙马*\n\n11. **用于图生成的深度生成模型系统综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.06686.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FGuo2020A.md)\n\n    *郭晓杰、赵亮*\n\n12. **图数据上的对抗攻击与防御：综述。** arXiv 2018 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.10528) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FSun2018Adversarial.md)\n\n    *孙立超、王吉、余Philip S.、李博*\n\n13. **自动化图机器学习：方法、库与发展方向。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.01288.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FWang2022Automated.md)\n\n    *王欣、张子威、朱文武*\n\n14. **弥合空间域与谱域之间的差距：图神经网络综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.11867) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FChen2020Bridging.md)\n\n    *陈志谦、陈芳兰、张磊、季涛然、傅凯群、赵亮、陈峰、卢长田*\n\n15. **基于图神经网络的深度强化学习中的挑战与机遇：算法与应用的全面回顾。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.07922.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FMunikoti2022Challenges.md)\n\n    *赛·穆尼科蒂、迪佩什·阿加瓦尔、拉娅·达斯、马汉特什·哈拉帕纳瓦尔、巴拉苏布拉马尼亚姆·纳塔拉詹*\n\n16. **图神经网络的计算：从算法到加速器的综述。** ACM Comput. Surv. 2022 [论文](http:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.00130.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FAbadal2022Computing.md)\n\n    *塞尔吉·阿巴达尔、阿克沙伊·贾因、罗伯特·吉拉多、豪尔赫·洛佩斯-阿隆索、爱德华·阿拉尔孔*\n\n17. **深度图相似度学习：综述。** Data Min. Knowl. Discov. 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.11615.pdf) [bib](\u002Fbib\u002FMachine-Learning\u002FGraph-Neural-Networks\u002FMa2021Deep.md)\n\n    *马贵祥、内斯琳·K·艾哈迈德、西奥多·L·威尔克、余Philip S.*\n\n18. **图上的深度学习：综述。** IEEE Trans. Knowl. 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Taylor, Peter Stone*\n\n14. **深度强化学习中的迁移学习：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07888) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FZhu2020Transfer.md)\n\n    *Zhuangdi Zhu, Kaixiang Lin, Jiayu Zhou*\n\n15. **深度学习中的可迁移性：综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.05867.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTransfer-Learning\u002FJiang2022Transferability.md)\n\n    *Junguang Jiang, Yang Shu, Jianmin Wang, Mingsheng Long*\n\n#### [可信机器学习](#content)\n\n1. **可信图神经网络综合综述：隐私、鲁棒性、公平性与可解释性。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.08570.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FDai2022A.md)\n\n    *Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, Suhang Wang*\n\n2. **深度学习中的神经后门攻击与防御综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.07183.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FWang2022A.md)\n\n    *Jie Wang, Ghulam Mubashar Hassan, Naveed Akhtar*\n\n3. **机器学习中的隐私攻击综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2007.07646) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FRigaki2020A.md)\n\n    *Maria Rigaki, Sebastian Garcia*\n\n4. **深度神经网络的安全性与可信性综述：验证、测试、对抗攻击与防御以及可解释性。** 计算机科学评论 2020 [论文](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1574013719302527?via%3Dihub) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FHuang2020A.md)\n\n    *Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, Xinping Yi*\n\n5. **机器学习中的偏见与公平性综述。** ACM计算综述 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.09635) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FMehrabi2022A.md)\n\n    *Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan*\n\n6. **后门学习：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.08745) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FLi2020Backdoor.md)\n\n    *Yiming Li, Baoyuan Wu, Yong Jiang, Zhifeng Li, Shu-Tao Xia*\n\n7. **差分隐私与机器学习：综述与评述。** arXiv 2014 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1412.7584) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FJi2014Differential.md)\n\n    *Zhanglong Ji, Zachary Chase Lipton, Charles Elkan*\n\n8. **机器学习中的公平性：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.04053.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FCaton2020Fairness.md)\n\n    *Simon Caton, Christian Haas*\n\n9. **局部差分隐私及其应用：综合综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.03686) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FYang2020Local.md)\n\n*孟孟·杨、凌娟·吕、俊·赵、天清·朱、郭炎·林*\n\n10. **深度学习中的隐私：综述。** arXiv 2020 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.12254) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FMireshghallah2020Privacy.md)\n\n    *法特梅赫萨达特·米雷什加拉、穆罕默德卡泽姆·塔拉姆、普拉尼特·维帕科马、阿比谢克·辛格、拉梅什·拉斯卡尔、哈迪·埃斯迈尔扎德*\n\n11. **机器学习安全的分类学：综述与入门。** ACM Comput. Surv. 2023 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.04823) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FMohseni2023Taxonomy.md)\n\n    *西纳·莫赫塞尼、郝涛·王、超伟·肖、志丁·俞、张阳·王、杰伊·亚达瓦*\n\n12. **机器学习系统的技术成熟度等级。** arXiv 2021 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.03989.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FLavin2021Technology.md)\n\n    *亚历山大·拉文、希兰·M·吉利根-李、阿莱西亚·维斯尼奇、西达·甘朱、达瓦·纽曼、苏乔伊·冈古利、丹尼·兰格、阿提利姆·居内斯·拜丁、阿米特·夏尔马、亚当·吉布森、亚林·加尔、埃里克·P·邢、克里斯·马特曼、詹姆斯·帕尔*\n\n13. **深度伪造的生成与检测：综述。** ACM Comput. Surv. 2022 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.11138) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FMirsky2022The.md)\n\n    *伊斯罗埃尔·米尔斯基、温克·李*\n\n14. **迈向透明的人工智能：深度神经网络内部结构解释的综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.13243.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FRäuker2022Toward.md)\n\n    *蒂尔曼·劳克尔、安森·霍、斯蒂芬·卡斯珀、迪伦·哈德菲尔德-梅内尔*\n\n15. **可信人工智能：从原则到实践。** ACM Comput. Surv. 2023 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.01167.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FLi2023Trustworthy.md)\n\n    *博·李、彭·齐、博·刘、帅·迪、金根·刘、季泉·裴、金峰·易、博文·周*\n\n16. **可信图神经网络：方面、方法与趋势。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.07424.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FZhang2022Trustworthy.md)\n\n    *何·张、邦·吴、兴亮·袁、诗睿·潘、杭杭·通、建·裴*\n\n17. **教程：安全可靠的机器学习。** arXiv 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.07204) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FSaria2019Tutorial.md)\n\n    *苏奇·萨里亚、阿达尔什·苏巴斯瓦米*\n\n18. **当机器学习与隐私相遇：综述与展望。** ACM Comput. Surv. 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.11819.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FLiu2022When.md)\n\n    *博·刘、明·丁、西纳·沙哈姆、温妮·拉哈尤、法哈德·法罗基、子怀·林*\n\n19. **野性模式再临：针对训练数据投毒的机器学习安全综述。** arXiv 2022 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.01992.pdf) [参考文献](\u002Fbib\u002FMachine-Learning\u002FTrustworthy-Machine-Learning\u002FCinà2022Wild.md)\n\n    *安东尼奥·埃马努埃莱·奇纳、卡特琳·格罗塞、安布拉·德蒙蒂斯、塞巴斯蒂亚诺·瓦斯孔、维尔纳·泽林格、伯恩哈德·阿洛伊斯·莫瑟、阿丽娜·奥普雷亚、巴蒂斯塔·比吉奥、马切洛·佩利洛、法比奥·罗利*\n\n\n\n\n## 团队成员\n\n该项目由以下机构维护：\n\n*东北大学计算机科学与工程学院自然语言处理实验室*\n\n*NiuTrans 研究团队*\n\n如有任何问题，欢迎随时联系我们（libei_neu [at] outlook.com）。\n\n## 致谢\n\n我们谨向为本项目做出贡献的各位表示感谢。他们是：\n\n*川浩·吕、凯燕·昌、子洋·王、书涵·周、诺·许、贝·李、银桥·李、权·杜、欣·曾、老胡·王、成龙·王、晓倩·刘、轩俊·周、京楠·张、永宇·穆、泽凡·周、艳红·江、新阳·朱、星宇·刘、东·毕、平·徐、子健·李、丰宁·田、慧·刘、凯·冯、宇豪·张、驰·胡、迪·杨、雷·郑、赫轩·陈、泽阳·王、腾波·刘、夏·孟、伟桥·山、陶·周、润哲·曹、英峰·罗、炳豪·魏、万迪·徐、艳·张、一超·王、梦雨·马、子豪·刘*","# ABigSurvey 快速上手指南\n\nABigSurvey 并非一个需要安装运行的软件工具，而是一个**开源的综述论文索引库**。它整理了自然语言处理（NLP）和机器学习（ML）领域的数百篇综述论文，并按主题分类。开发者主要通过访问其在线列表或克隆仓库来获取资源。\n\n## 环境准备\n\n本项目无需特定的运行环境或依赖库。您只需要：\n- **操作系统**：Windows、macOS 或 Linux 均可。\n- **必要工具**：\n  - 现代浏览器（用于直接查看在线列表）。\n  - Git（可选，用于克隆仓库到本地）。\n- **网络环境**：由于原始仓库托管在 GitHub，国内用户建议配置加速代理或使用镜像源，以确保克隆速度。\n\n## 安装步骤（获取资源）\n\n您可以选择直接在线浏览或克隆到本地。\n\n### 方式一：在线浏览（推荐）\n直接访问项目维护的在线文档或特定子项目链接，无需任何操作：\n- **主项目（NLP & ML 综述）**：访问 GitHub 仓库页面查看 `README` 中的分类列表。\n- **大语言模型专题（LLM Surveys）**：[点击此处访问](https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurveyOfLLMs)\n\n### 方式二：克隆到本地\n如果您希望离线查看或贡献内容，请使用以下命令克隆仓库。\n\n**使用国内镜像加速克隆（推荐）：**\n```bash\ngit clone https:\u002F\u002Fgitee.com\u002Fmirrors\u002FABigSurvey.git\n# 注意：如果上述镜像不存在，请使用标准克隆并配合梯子，或尝试以下通用加速技巧\n# git clone https:\u002F\u002Fgithub.com.cnpmjs.org\u002FNiuTrans\u002FABigSurvey.git\n```\n\n**标准克隆命令：**\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurvey.git\ncd ABigSurvey\n```\n\n## 基本使用\n\n获取资源后，您可以通过以下方式查找所需的综述论文：\n\n### 1. 浏览分类列表\n打开项目根目录下的 `README.md` 文件（或在 GitHub 网页端查看），您会看到详细的分类目录。主要涵盖两大领域：\n\n- **Natural Language Processing (NLP)**\n  包含计算社会科学、对话系统、文本生成、信息抽取、知识图谱、大语言模型 (LLM)、机器翻译、命名实体识别、预训练模型、Prompt 工程等 20+ 个子领域。\n  \n- **Machine Learning (ML)**\n  包含架构搜索、AutoML、贝叶斯方法、计算机视觉、对比学习、扩散模型 (Diffusion Models)、联邦学习、少样本学习、图神经网络、模型压缩、迁移学习等 20+ 个子领域。\n\n### 2. 查找具体论文\n在每个分类标题下（例如 `#### [Dialogue and Interactive Systems]`），列出了相关的综述论文。每条记录包含：\n- **论文标题**\n- **发表 venue 与年份**\n- **[paper]**：指向论文 PDF 或 arXiv 页面的链接。\n- **[bib]**：指向 BibTeX 引用文件的链接。\n\n**使用示例：**\n假设您需要查找关于“情感分析”的综述：\n1. 在文档中定位到 `Sentiment Analysis, Stylistic Analysis and Argument Mining` 章节。\n2. 点击该章节下列出的 `[paper]` 链接即可直接阅读论文。\n3. 点击 `[bib]` 链接可获取引用格式，方便写入您的 LaTeX 或参考文献管理工具。\n\n### 3. 查看统计图表\n项目中包含了关于各领域论文数量分布、年度发表趋势以及热点话题词云的图片（Figure 1-5），直接在 `README` 中即可查看，帮助您快速了解领域热度。","某高校人工智能实验室的博士生李明正在筹备关于“大语言模型在医疗领域应用”的综述论文，急需全面掌握该细分方向的研究现状与演进脉络。\n\n### 没有 ABigSurvey 时\n- **检索效率低下**：需要在 Google Scholar、arXiv 和各类会议网站间反复切换搜索，耗时数天仍难以确认是否遗漏了关键综述。\n- **分类体系混乱**：找到的文献主题交叉严重，缺乏统一的标准分类（如区分“提示工程”与“微调技术”），导致文献整理逻辑不清。\n- **视野存在盲区**：容易局限于自己熟悉的子领域，难以发现跨学科关联（如机器学习中的“联邦学习”如何应用于医疗 NLP），影响论文的深度。\n- **验证工作繁琐**：难以快速统计某一具体问题的研究热度或发展趋势，缺乏现成的数据支持来论证选题价值。\n\n### 使用 ABigSurvey 后\n- **一站式获取资源**：直接通过 ABigSurvey 定位到\"Large Language Models\"及\"NLP Applications\"类别，瞬间获取数十篇高质量相关综述链接，将资料收集时间从几天缩短至几小时。\n- **依托权威分类**：借助其遵循 ACL\u002FICML 标准的详细分类体系，迅速理清“医疗实体识别”、“临床对话系统”等子方向的边界，构建出逻辑严密的论文大纲。\n- **拓展研究视野**：通过浏览“联邦学习”、“可解释性分析”等相邻章节，意外发现了隐私保护与模型透明度在医疗场景下的结合点，提升了论文的创新性。\n- **量化趋势分析**：利用项目中提供的简单计数和问题列表，快速引用数据说明该领域的增长趋势，为引言部分提供了有力的实证支持。\n\nABigSurvey 将研究者从繁琐的文献大海捞针中解放出来，使其能专注于高价值的学术洞察与理论创新。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNiuTrans_ABigSurvey_11989a8f.png","NiuTrans","NiuTrans Open Source","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FNiuTrans_abba007d.png","The NiuTrans Open Source (NOS)  project, maintained  by NLP Lab at Northeastern University and NiuTrans Research, aims at developing cutting edge NLP systems.",null,"niutrans@mail.neu.edu.cn","https:\u002F\u002Fgithub.com\u002FNiuTrans",2033,246,"2026-04-10T04:55:16","GPL-3.0",1,"","未说明",{"notes":87,"python":85,"dependencies":88},"该项目是一个综述论文列表（Survey of Surveys），主要包含论文标题、链接和分类信息，并非可执行的软件代码库，因此没有特定的操作系统、GPU、内存或 Python 依赖要求。用户只需通过浏览器查看 README 或访问提供的论文链接即可使用。",[],[14,35],[91,92,93,94,95,96],"natural-language-processing","machine-learning","deep-learning","neural-networks","paper-list","surveys","2026-03-27T02:49:30.150509","2026-04-15T03:23:18.233110",[100,105,110,115,119,124],{"id":101,"question_zh":102,"answer_zh":103,"source_url":104},33537,"仓库内容太多难以浏览，是否会添加目录导航？","维护者已收到关于导航困难的反馈，并承诺很快会在 README 文件的开头添加带有锚点链接的目录（Table of Contents），以便用户更方便地跳转到不同章节。","https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurvey\u002Fissues\u002F2",{"id":106,"question_zh":107,"answer_zh":108,"source_url":109},33538,"除了文本排序（Text Ranking），是否接受其他 NLP 子领域（如文本简化、问题生成）的综述？","接受的。该列表不仅限于特定子领域，维护者对文本简化（Text Simplification）、问题生成（Question Generation）等其他 NLP 领域的综述也持开放态度，欢迎用户贡献相关论文。","https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurvey\u002Fissues\u002F1",{"id":111,"question_zh":112,"answer_zh":113,"source_url":114},33539,"如果我通过 Issue 推荐了论文，后续是由我来修改还是由维护者修改？","这取决于您的选择。您可以选择自己创建 PR 进行修改（这是推荐做法），也可以仅在 Issue 中提出建议，这种情况下维护者表示他们会在下一次更新时亲自将论文添加到列表中。","https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurvey\u002Fissues\u002F12",{"id":116,"question_zh":117,"answer_zh":118,"source_url":109},33534,"我想推荐一篇新的综述论文，应该直接提交 Issue 还是创建 Pull Request（PR）？","如果您有具体的论文想要添加，维护者非常欢迎您直接创建 Pull Request (PR) 并提交，他们会尽快合并。当然，您也可以先在 Issue 中提出建议，维护者会在下一次更新时添加，但直接提交 PR 是更受鼓励的方式。",{"id":120,"question_zh":121,"answer_zh":122,"source_url":123},33535,"列表中可以包含中文撰写的综述论文吗？","可以的。维护者明确表示欢迎添加中文论文。如果您有相关的中文综述论文资源，欢迎提供给他们，他们很快就会将其添加到列表中。","https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurvey\u002Fissues\u002F5",{"id":125,"question_zh":126,"answer_zh":127,"source_url":128},33536,"如果某篇综述论文发表了正式的期刊版本，列表中的链接会更新吗？","会的。如果您发现列表中的论文链接仍是 arXiv 版本，而已有更新的正式期刊版本链接，请通过 Issue 告知维护者。他们会核实并尽快将链接更新为正式的期刊论文地址。","https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurvey\u002Fissues\u002F3",[]]