机器学习 | 最全综述列表！

本文转载自公众号Datawhale

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。

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]

参考

[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/ht

[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=829418

[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

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