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前言

 

一個『機器學習領域綜述大列表』，涵蓋了自然語言處理、推薦系統(tǒng)、計算機視覺、深度學習、強化學習等主題。另外發(fā)現(xiàn)源repo中NLP相關的綜述不是很多，于是把一些覺得還不錯的文章添加進去了，重新整理更新在 AI-Surveys 中。

作者丨kaiyuan 轉載自 | NewBeeNLP

• ml-surveys: https://github.com/eugeneyan/ml-surveys

• AI-Surveys: https://github.com/KaiyuanGao/AI-Surveys

『收藏等于看完』系列，來看看都有哪些吧， enjoy！

自然語言處理

• 深度學習：Recent Trends in Deep Learning Based Natural Language Processing[2]

• 文本分類：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]

• 遷移學習：Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer[6] (Paper[7])

• 遷移學習：Neural Transfer Learning for Natural Language Processing[8]

• 知識圖譜：A Survey on Knowledge Graphs: Representation, Acquisition and Applications[9]

• 命名實體識別：A Survey on Deep Learning for Named Entity Recognition[10]

• 關系抽取：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]

• 閱讀理解：Neural Reading Comprehension And Beyond[15]

• 閱讀理解：Neural Machine Reading Comprehension: Methods and Trends[16]

• 機器翻譯：Neural Machine Translation: A Review[17]

• 機器翻譯：A Survey of Domain Adaptation for Neural Machine Translation[18]

• 預訓練模型：Pre-trained Models for Natural Language Processing: A Survey[19]

• 注意力機制：An Attentive Survey of Attention Models[20]

• 注意力機制：An Introductory Survey on Attention Mechanisms in NLP Problems[21]

• 注意力機制：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]

推薦系統(tǒng)

• 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]

深度學習

• A State-of-the-Art Survey on Deep Learning Theory and Architectures[32]

• 知識蒸餾：Knowledge Distillation: A Survey[33]

• 模型壓縮：Compression of Deep Learning Models for Text: A Survey[34]

• 遷移學習：A Survey on Deep Transfer Learning[35]

• 神經架構搜索：A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions[36]

• 神經架構搜索：Neural Architecture Search: A Survey[37]

計算機視覺

• 目標檢測：Object Detection in 20 Years[38]

• 對抗性攻擊：Threat of Adversarial Attacks on Deep Learning in Computer Vision[39]

• 自動駕駛：Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art[40]

強化學習

• 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

• 圖：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]

本文參考文獻

[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

   
   

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