強(qiáng) 到 沒(méi) 朋 友！機(jī)器學(xué)習(xí)領(lǐng)域最全綜述列表！
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繼續(xù)來(lái)給大家分享github上的干貨，一個(gè)『機(jī)器學(xué)習(xí)領(lǐng)域綜述大列表』，涵蓋了自然語(yǔ)言處理、推薦系統(tǒng)、計(jì)算機(jī)視覺(jué)、深度學(xué)習(xí)、強(qiáng)化學(xué)習(xí)等主題。
另外發(fā)現(xiàn)源repo中NLP相關(guān)的綜述不是很多，于是把一些覺(jué)得還不錯(cuò)的文章添加進(jìn)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，來(lái)看看都有哪些吧， enjoy！
自然語(yǔ)言處理
深度學(xué)習(xí)：Recent Trends in Deep Learning Based Natural Language Processing[2] 文本分類(lèi)：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(xué)習(xí)：Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer[6] (Paper[7]) 遷移學(xué)習(xí)：Neural Transfer Learning for Natural Language Processing[8] 知識(shí)圖譜：A Survey on Knowledge Graphs: Representation, Acquisition and Applications[9] 命名實(shí)體識(shí)別：A Survey on Deep Learning for Named Entity Recognition[10] 關(guān)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(jī)器翻譯：Neural Machine Translation: A Review[17] 機(jī)器翻譯：A Survey of Domain Adaptation for Neural Machine Translation[18] 預(yù)訓(xùn)練模型：Pre-trained Models for Natural Language Processing: A Survey[19] 注意力機(jī)制：An Attentive Survey of Attention Models[20] 注意力機(jī)制：An Introductory Survey on Attention Mechanisms in NLP Problems[21] 注意力機(jī)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(xué)習(xí)
A State-of-the-Art Survey on Deep Learning Theory and Architectures[32] 知識(shí)蒸餾：Knowledge Distillation: A Survey[33] 模型壓縮：Compression of Deep Learning Models for Text: A Survey[34] 遷移學(xué)習(xí)：A Survey on Deep Transfer Learning[35] 神經(jīng)架構(gòu)搜索：A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions[36] 神經(jīng)架構(gòu)搜索：Neural Architecture Search: A Survey[37]
計(jì)算機(jī)視覺(jué)
目標(biāo)檢測(cè)：Object Detection in 20 Years[38] 對(duì)抗性攻擊：Threat of Adversarial Attacks on Deep Learning in Computer Vision[39] 自動(dòng)駕駛：Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art[40]
強(qiáng)化學(xué)習(xí)
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]
本文參考資料
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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