机器学习领域最全综述列表

继续来给大家分享github上的干货,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]

本文参考资料

[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]Pre-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

原文:https://mp.weixin.qq.com/s/c_ERUxFWLu7flDlWMnsuzw

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