ml-surveys

It's hard to keep up with the latest and greatest in machine learning. Here's a selection of survey papers summarizing the advances in the field.

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Table of Contents

• Recommendation
• Deep Learning
• Natural Language Processing
• Computer Vision
• Reinforcement Learning
• Embeddings
• Meta-learning and Few-shot Learning
• Others

Recommendation

• 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

Deep Learning

• 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

Natural Language Processing

• 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

Computer Vision

• 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

Reinforcement Learning

• 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

Embeddings

• 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

Others

• Transfer learning: A Survey on Transfer Learning