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各机器学习领域综述清单！

ppythonic生物人约 9 分钟
一个『机器学习领域综述大列表』， 涵盖了自然语言处理、推荐系统、计算机视觉、深度学习、强化学习等主题。
另外发现源repo中NLP相关的综述不是很多，于是把一些觉得还不错的文章添加进去了，重新整理更新在 AI-Surveys[1] 中。
  • ml-surveys : https://github.com/eugeneyan/ml-surveys
  • AI-Surveys : https://github.com/KaiyuanGao/AI-Surveys

『收藏等于看完』系列，来看看都有哪些吧， enjoy！

自然语言处理

  • 深度学习：Recent Trends in Deep Learning Based Natural Language Processing[2]
  • 文本分类：Deep Learning Based Text Classification: A Comprehensive Review[3]
  • 文本生成：Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation[4]
  • 文本生成：Neural Language Generation: Formulation, Methods, and Evaluation[5]
  • 迁移学习：Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer[6] (Paper[7])
  • 迁移学习：Neural Transfer Learning for Natural Language Processing[8]
  • 知识图谱：A Survey on Knowledge Graphs: Representation, Acquisition and Applications[9]
  • 命名实体识别：A Survey on Deep Learning for Named Entity Recognition[10]
  • 关系抽取：More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction[11]
  • 情感分析：Deep Learning for Sentiment Analysis : A Survey[12]
  • ABSA情感分析：Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges[13]
  • 文本匹配：Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering[14]
  • 阅读理解：Neural Reading Comprehension And Beyond[15]
  • 阅读理解：Neural Machine Reading Comprehension: Methods and Trends[16]
  • 机器翻译：Neural Machine Translation: A Review[17]
  • 机器翻译：A Survey of Domain Adaptation for Neural Machine Translation[18]
  • 预训练模型：Pre-trained Models for Natural Language Processing: A Survey[19]
  • 注意力机制：An Attentive Survey of Attention Models[20]
  • 注意力机制：An Introductory Survey on Attention Mechanisms in NLP Problems[21]
  • 注意力机制：Attention in Natural Language Processing[22]
  • BERT：A Primer in BERTology: What we know about how BERT works[23]
  • Beyond Accuracy: Behavioral Testing of NLP Models with CheckList[24]
  • Evaluation of Text Generation: A Survey[25]

推荐系统

  • Recommender systems survey[26]
  • Deep Learning based Recommender System: A Survey and New Perspectives[27]
  • Are We Really Making Progress? A Worrying Analysis of Neural Recommendation Approaches[28]
  • A Survey of Serendipity in Recommender Systems[29]
  • Diversity in Recommender Systems – A survey[30]
  • A Survey of Explanations in Recommender Systems[31]

深度学习

  • A State-of-the-Art Survey on Deep Learning Theory and Architectures[32]
  • 知识蒸馏：Knowledge Distillation: A Survey[33]
  • 模型压缩：Compression of Deep Learning Models for Text: A Survey[34]
  • 迁移学习：A Survey on Deep Transfer Learning[35]
  • 神经架构搜索：A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions[36]
  • 神经架构搜索：Neural Architecture Search: A Survey[37]

计算机视觉

  • 目标检测：Object Detection in 20 Years[38]
  • 对抗性攻击：Threat of Adversarial Attacks on Deep Learning in Computer Vision[39]
  • 自动驾驶：Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art[40]

强化学习

  • A Brief Survey of Deep Reinforcement Learning[41]
  • Transfer Learning for Reinforcement Learning Domains[42]
  • Review of Deep Reinforcement Learning Methods and Applications in Economics[43]

Embeddings

  • 图：A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications[44]
  • 文本：From Word to Sense Embeddings:A Survey on Vector Representations of Meaning[45]
  • 文本：Diachronic Word Embeddings and Semantic Shifts[46]
  • 文本：Word Embeddings: A Survey[47]
  • A Survey on Contextual Embeddings[48]

Meta-learning & Few-shot Learning

  • A Survey on Knowledge Graphs: Representation, Acquisition and Applications[49]
  • Meta-learning for Few-shot Natural Language Processing: A Survey[50]
  • Learning from Few Samples: A Survey[51]
  • Meta-Learning in Neural Networks: A Survey[52]
  • A Comprehensive Overview and Survey of Recent Advances in Meta-Learning[53]
  • Baby steps towards few-shot learning with multiple semantics[54]
  • Meta-Learning: A Survey[55]
  • A Perspective View And Survey Of Meta-learning[56]

其他

  • A Survey on Transfer Learning[57]

本文参考文献

[1] AI-Surveys : https://github.com/KaiyuanGao/AI-Surveys [2] Recent Trends in Deep Learning Based Natural Language Processing : https://arxiv.org/pdf/1708.02709.pdf [3] Deep Learning Based Text Classification: A Comprehensive Review : https://arxiv.org/pdf/2004.03705 [4] Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation : https://www.jair.org/index.php/jair/article/view/11173/26378 [5] Neural Language Generation: Formulation, Methods, and Evaluation : https://arxiv.org/pdf/2007.15780.pdf [6] Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer : https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html [7] Paper : https://arxiv.org/abs/1910.10683 [8] Neural Transfer Learning for Natural Language Processing : https://aran.library.nuigalway.ie/handle/10379/15463 [9] A Survey on Knowledge Graphs: Representation, Acquisition and Applications : https://arxiv.org/abs/2002.00388 [10] A Survey on Deep Learning for Named Entity Recognition : https://arxiv.org/abs/1812.09449 [11] More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction : https://arxiv.org/abs/2004.03186 [12] Deep Learning for Sentiment Analysis : A Survey : https://arxiv.org/abs/1801.07883 [13] Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8726353 [14] Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering : https://www.aclweb.org/anthology/C18-1328/ [15] Neural Reading Comprehension And Beyond : https://stacks.stanford.edu/file/druid:gd576xb1833/thesis-augmented.pdf [16] Neural Machine Reading Comprehension: Methods and Trends : https://arxiv.org/abs/1907.01118 [17] Neural Machine Translation: A Review : https://arxiv.org/abs/1912.02047 [18] A Survey of Domain Adaptation for Neural Machine Translation : https://www.aclweb.org/anthology/C18-1111.pdf [19]P re-trained Models for Natural Language Processing: A Survey : https://arxiv.org/abs/2003.08271 [20] An Attentive Survey of Attention Models : https://arxiv.org/pdf/1904.02874.pdf [21] An Introductory Survey on Attention Mechanisms in NLP Problems : https://arxiv.org/abs/1811.05544 [22] Attention in Natural Language Processing : https://arxiv.org/abs/1902.02181 [23] A Primer in BERTology: What we know about how BERT works : https://arxiv.org/pdf/2002.12327.pdf [24] Beyond Accuracy: Behavioral Testing of NLP Models with CheckList : https://arxiv.org/pdf/2005.04118.pdf [25] Evaluation of Text Generation: A Survey : https://arxiv.org/pdf/2006.14799.pdf [26] Recommender systems survey : http://irntez.ir/wp-content/uploads/2016/12/sciencedirec.pdf [27] Deep Learning based Recommender System: A Survey and New Perspectives : https://arxiv.org/pdf/1707.07435.pdf [28] Are We Really Making Progress? A Worrying Analysis of Neural Recommendation Approaches : https://arxiv.org/pdf/1907.06902.pdf [29] A Survey of Serendipity in Recommender Systems : https://www.researchgate.net/publication/306075233_A_Survey_of_Serendipity_in_Recommender_Systems [30] Diversity in Recommender Systems – A survey : https://papers-gamma.link/static/memory/pdfs/153-Kunaver_Diversity_in_Recommender_Systems_2017.pdf [31] A Survey of Explanations in Recommender Systems : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.418.9237&rep=rep1&type=pdf [32] A State-of-the-Art Survey on Deep Learning Theory and Architectures : https://www.mdpi.com/2079-9292/8/3/292/htm [33] Knowledge Distillation: A Survey : https://arxiv.org/pdf/2006.05525.pdf [34] Compression of Deep Learning Models for Text : A Survey: https://arxiv.org/pdf/2008.05221.pdf [35] A Survey on Deep Transfer Learning : https://arxiv.org/pdf/1808.01974.pdf [36] A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions : https://arxiv.org/abs/2006.02903 [37] Neural Architecture Search: A Survey : https://arxiv.org/abs/1808.05377 [38] Object Detection in 20 Years : https://arxiv.org/pdf/1905.05055.pdf [39] Threat of Adversarial Attacks on Deep Learning in Computer Vision : https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8294186 [40] Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art : https://arxiv.org/pdf/1704.05519.pdf [41] A Brief Survey of Deep Reinforcement Learning : https://arxiv.org/pdf/1708.05866.pdf [42] Transfer Learning for Reinforcement Learning Domains : http://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf [43] Review of Deep Reinforcement Learning Methods and Applications in Economics : https://arxiv.org/pdf/2004.01509.pdf [44] A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications : https://arxiv.org/pdf/1709.07604 [45] From Word to Sense Embeddings:A Survey on Vector Representations of Meaning : https://www.jair.org/index.php/jair/article/view/11259/26454 [46] Diachronic Word Embeddings and Semantic Shifts : https://arxiv.org/pdf/1806.03537.pdf [47] Word Embeddings: A Survey : https://arxiv.org/abs/1901.09069 [48] A Survey on Contextual Embeddings : https://arxiv.org/abs/2003.07278 [49] A Survey on Knowledge Graphs: Representation, Acquisition and Applications : https://arxiv.org/abs/2002.00388 [50] Meta-learning for Few-shot Natural Language Processing: A Survey : https://arxiv.org/abs/2007.09604 [51] Learning from Few Samples: A Survey : https://arxiv.org/abs/2007.15484 [52] Meta-Learning in Neural Networks: A Survey : https://arxiv.org/abs/2004.05439 [53] A Comprehensive Overview and Survey of Recent Advances in Meta-Learning : https://arxiv.org/abs/2004.11149 [54] Baby steps towards few-shot learning with multiple semantics : https://arxiv.org/abs/1906.01905 [55] Meta-Learning: A Survey : https://arxiv.org/abs/1810.03548 [56] A Perspective View And Survey Of Meta-learning : https://www.researchgate.net/publication/2375370_A_Perspective_View_And_Survey_Of_Meta-Learning [57] A Survey on Transfer Learning : http://202.120.39.19:40222/wp-content/uploads/2018/03/A-Survey-on-Transfer-Learning.pdf 作者：kaiyuan，来源：NewBeeNLP END- 微信交流 备注来意 (交流、合作等等)

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