机器学习领域最全综述列表!
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每日干货 & 每月组队学习 ,不错过
Datawhale干货
作者:kaiyuan,来源:NewBeeNLP
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。
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ml-surveys: https://github.com/eugeneyan/ml-surveys
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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
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深度学习:Recent Trends in Deep Learning Based Natural Language Processing [2]
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文本分类:Deep Learning Based Text Classification: A Comprehensive Review [3]
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文本生成:Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation [4]
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文本生成:Neural Language Generation: Formulation, Methods, and Evaluation [5]
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迁移学习:Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer [6] (Paper [7] )
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迁移学习:Neural Transfer Learning for Natural Language Processing [8]
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知识图谱:A Survey on Knowledge Graphs: Representation, Acquisition and Applications [9]
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命名实体识别:A Survey on Deep Learning for Named Entity Recognition [10]
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关系抽取:More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction [11]
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情感分析:Deep Learning for Sentiment Analysis : A Survey [12]
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ABSA情感分析:Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges [13]
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文本匹配:Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering [14]
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阅读理解:Neural Reading Comprehension And Beyond [15]
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阅读理解:Neural Machine Reading Comprehension: Methods and Trends [16]
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机器翻译:Neural Machine Translation: A Review [17]
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机器翻译:A Survey of Domain Adaptation for Neural Machine Translation [18]
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预训练模型:Pre-trained Models for Natural Language Processing: A Survey [19]
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注意力机制:An Attentive Survey of Attention Models [20]
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注意力机制:An Introductory Survey on Attention Mechanisms in NLP Problems [21]
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注意力机制:Attention in Natural Language Processing [22]
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BERT:A Primer in BERTology: What we know about how BERT works [23]
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Beyond Accuracy: Behavioral Testing of NLP Models with CheckList [24]
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Evaluation of Text Generation: A Survey [25]
推荐系统
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Recommender systems survey [26]
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Deep Learning based Recommender System: A Survey and New Perspectives [27]
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Are We Really Making Progress? A Worrying Analysis of Neural Recommendation Approaches [28]
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A Survey of Serendipity in Recommender Systems [29]
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Diversity in Recommender Systems – A survey [30]
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A Survey of Explanations in Recommender Systems [31]
深度学习
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A State-of-the-Art Survey on Deep Learning Theory and Architectures [32]
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知识蒸馏:Knowledge Distillation: A Survey [33]
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模型压缩:Compression of Deep Learning Models for Text: A Survey [34]
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迁移学习:A Survey on Deep Transfer Learning [35]
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神经架构搜索:A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions [36]
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神经架构搜索:Neural Architecture Search: A Survey [37]
计算机视觉
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目标检测:Object Detection in 20 Years [38]
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对抗性攻击:Threat of Adversarial Attacks on Deep Learning in Computer Vision [39]
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自动驾驶:Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art [40]
强化学习
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A Brief Survey of Deep Reinforcement Learning [41]
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Transfer Learning for Reinforcement Learning Domains [42]
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Review of Deep Reinforcement Learning Methods and Applications in Economics [43]
Embeddings
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图:A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications [44]
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文本:From Word to Sense Embeddings:A Survey on Vector Representations of Meaning [45]
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文本:Diachronic Word Embeddings and Semantic Shifts [46]
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文本:Word Embeddings: A Survey [47]
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A Survey on Contextual Embeddings [48]
Meta-learning & Few-shot Learning
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A Survey on Knowledge Graphs: Representation, Acquisition and Applications [49]
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Meta-learning for Few-shot Natural Language Processing: A Survey [50]
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Learning from Few Samples: A Survey [51]
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Meta-Learning in Neural Networks: A Survey [52]
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A Comprehensive Overview and Survey of Recent Advances in Meta-Learning [53]
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Baby steps towards few-shot learning with multiple semantics [54]
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Meta-Learning: A Survey [55]
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A Perspective View And Survey Of Meta-learning [56]
其他
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A Survey on Transfer Learning [57]
本文参考资料
AI-Surveys: https://github.com/KaiyuanGao/AI-Surveys
Recent Trends in Deep Learning Based Natural Language Processing: https://arxiv.org/pdf/1708.02709.pdf
Deep Learning Based Text Classification: A Comprehensive Review: https://arxiv.org/pdf/2004.03705
Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation: https://www.jair.org/index.php/jair/article/view/11173/26378
Neural Language Generation: Formulation, Methods, and Evaluation: https://arxiv.org/pdf/2007.15780.pdf
Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer: https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html
Paper: https://arxiv.org/abs/1910.10683
Neural Transfer Learning for Natural Language Processing: https://aran.library.nuigalway.ie/handle/10379/15463
A Survey on Knowledge Graphs: Representation, Acquisition and Applications: https://arxiv.org/abs/2002.00388
A Survey on Deep Learning for Named Entity Recognition: https://arxiv.org/abs/1812.09449
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction: https://arxiv.org/abs/2004.03186
Deep Learning for Sentiment Analysis : A Survey: https://arxiv.org/abs/1801.07883
Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8726353
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering: https://www.aclweb.org/anthology/C18-1328/
Neural Reading Comprehension And Beyond: https://stacks.stanford.edu/file/druid:gd576xb1833/thesis-augmented.pdf
Neural Machine Reading Comprehension: Methods and Trends: https://arxiv.org/abs/1907.01118
Neural Machine Translation: A Review: https://arxiv.org/abs/1912.02047
A Survey of Domain Adaptation for Neural Machine Translation: https://www.aclweb.org/anthology/C18-1111.pdf
Pre-trained Models for Natural Language Processing: A Survey: https://arxiv.org/abs/2003.08271
An Attentive Survey of Attention Models: https://arxiv.org/pdf/1904.02874.pdf
An Introductory Survey on Attention Mechanisms in NLP Problems: https://arxiv.org/abs/1811.05544
Attention in Natural Language Processing: https://arxiv.org/abs/1902.02181
A Primer in BERTology: What we know about how BERT works: https://arxiv.org/pdf/2002.12327.pdf
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList: https://arxiv.org/pdf/2005.04118.pdf
Evaluation of Text Generation: A Survey: https://arxiv.org/pdf/2006.14799.pdf
Recommender systems survey: http://irntez.ir/wp-content/uploads/2016/12/sciencedirec.pdf
Deep Learning based Recommender System: A Survey and New Perspectives: https://arxiv.org/pdf/1707.07435.pdf
Are We Really Making Progress? A Worrying Analysis of Neural Recommendation Approaches: https://arxiv.org/pdf/1907.06902.pdf
A Survey of Serendipity in Recommender Systems: https://www.researchgate.net/publication/306075233_A_Survey_of_Serendipity_in_Recommender_Systems
Diversity in Recommender Systems – A survey: https://papers-gamma.link/static/memory/pdfs/153-Kunaver_Diversity_in_Recommender_Systems_2017.pdf
A Survey of Explanations in Recommender Systems: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.418.9237&rep=rep1&type=pdf
A State-of-the-Art Survey on Deep Learning Theory and Architectures: https://www.mdpi.com/2079-9292/8/3/292/htm
Knowledge Distillation: A Survey: https://arxiv.org/pdf/2006.05525.pdf
Compression of Deep Learning Models for Text: A Survey: https://arxiv.org/pdf/2008.05221.pdf
A Survey on Deep Transfer Learning: https://arxiv.org/pdf/1808.01974.pdf
A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions: https://arxiv.org/abs/2006.02903
Neural Architecture Search: A Survey: https://arxiv.org/abs/1808.05377
Object Detection in 20 Years: https://arxiv.org/pdf/1905.05055.pdf
Threat of Adversarial Attacks on Deep Learning in Computer Vision: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8294186
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art: https://arxiv.org/pdf/1704.05519.pdf
A Brief Survey of Deep Reinforcement Learning: https://arxiv.org/pdf/1708.05866.pdf
Transfer Learning for Reinforcement Learning Domains: http://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf
Review of Deep Reinforcement Learning Methods and Applications in Economics: https://arxiv.org/pdf/2004.01509.pdf
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications: https://arxiv.org/pdf/1709.07604
From Word to Sense Embeddings:A Survey on Vector Representations of Meaning: https://www.jair.org/index.php/jair/article/view/11259/26454
Diachronic Word Embeddings and Semantic Shifts: https://arxiv.org/pdf/1806.03537.pdf
Word Embeddings: A Survey: https://arxiv.org/abs/1901.09069
A Survey on Contextual Embeddings: https://arxiv.org/abs/2003.07278
A Survey on Knowledge Graphs: Representation, Acquisition and Applications: https://arxiv.org/abs/2002.00388
Meta-learning for Few-shot Natural Language Processing: A Survey: https://arxiv.org/abs/2007.09604
Learning from Few Samples: A Survey: https://arxiv.org/abs/2007.15484
Meta-Learning in Neural Networks: A Survey: https://arxiv.org/abs/2004.05439
A Comprehensive Overview and Survey of Recent Advances in Meta-Learning: https://arxiv.org/abs/2004.11149
Baby steps towards few-shot learning with multiple semantics: https://arxiv.org/abs/1906.01905
Meta-Learning: A Survey: https://arxiv.org/abs/1810.03548
A Perspective View And Survey Of Meta-learning: https://www.researchgate.net/publication/2375370_A_Perspective_View_And_Survey_Of_Meta-Learning
A Survey on Transfer Learning: http://202.120.39.19:40222/wp-content/uploads/2018/03/A-Survey-on-Transfer-Learning.pdf
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