机器学习领域最全综述列表！（转载文章）

心已赠人 2023-09-27 10:56 1阅读 0赞

**本文来源：https://mp.weixin.qq.com/s/BA5maDBtZ6rpZ7SHSeCxDA**

本文给大家分享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\[57\]

### 本文参考文献 ###

\[1\]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