机器学习领域各领域必读经典综述论文整理分享

机器学习是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。

机器学习及其相关领域，如深度学习、自然语言处理、计算机视觉、推荐系统、强化学习等领域最近几年非常火，每年各式各样的国际顶会，投稿数每年都会海量增加。要持续Follow这些领域最新的技术，刷遍各大会议最新会议非常费时费力，特别是对于刚入门的同学。因此，为了方便同学们了解机器学习、AI各领域的最新的技术全貌，本资源整理了各领域必读的经典综述论文，分享给大家。

资源整理自网络，源地址：https://github.com/eugeneyan/ml-surveys

目录

推荐系统

Algorithms: Recommender systems survey

Algorithms: Deep Learning based Recommender System: A Survey and New Perspectives

Algorithms: Are We Really Making Progress? A Worrying Analysis of Neural Recommendation Approaches

Serendipity: A Survey of Serendipity in Recommender Systems

Diversity: Diversity in Recommender Systems – A survey

Explanations: A Survey of Explanations in Recommender Systems

深度学习

Architecture: A State-of-the-Art Survey on Deep Learning Theory and Architectures

Knowledge distillation: Knowledge Distillation: A Survey

Model compression: Compression of Deep Learning Models for Text: A Survey

Transfer learning: A Survey on Deep Transfer Learning

Neural architecture search: A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions

Neural architecture search: Neural Architecture Search: A Survey

自然语言处理

Deep Learning: Recent Trends in Deep Learning Based Natural Language Processing

Classification: Deep Learning Based Text Classification: A Comprehensive Review

Generation: Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation

Generation: Neural Language Generation: Formulation, Methods, and Evaluation

Transfer learning: Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer (Paper)

Metrics: Beyond Accuracy: Behavioral Testing of NLP Models with CheckList

Metrics: Evaluation of Text Generation: A Survey

计算机视觉

Object detection: Object Detection in 20 Years

Adversarial attacks: Threat of Adversarial Attacks on Deep Learning in Computer Vision

Autonomous vehicles: Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

深度强化学习

Algorithms: A Brief Survey of Deep Reinforcement Learning

Transfer learning: Transfer Learning for Reinforcement Learning Domains

Economics: Review of Deep Reinforcement Learning Methods and Applications in Economics

向量化技术

Graph: A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications

Text: From Word to Sense Embeddings:A Survey on Vector Representations of Meaning

Text: Diachronic Word Embeddings and Semantic Shifts

Text: Word Embeddings: A Survey

Meta-learning and Few-shot Learning

NLP: Meta-learning for Few-shot Natural Language Processing: A Survey

Domain Agnostic: Learning from Few Samples: A Survey

NN: Meta-Learning in Neural Networks: A Survey

Domain Agnostic: A Comprehensive Overview and Survey of Recent Advances in Meta-Learning

Domain Agnostic: Baby steps towards few-shot learning with multiple semantics

Domain Agnostic: Meta-Learning: A Survey

Domain Agnostic: A Perspective View And Survey Of Meta-learning

迁移学习

Transfer learning: A Survey on Transfer Learning

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