doraemon-rnd/ml-surveys

📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.

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
• Vision and Language
• Reinforcement Learning
• Graph
• Embeddings
• Meta-learning and Few-shot Learning
• Others

Recommendation

• Algorithms: Recommender systems survey (2013)
• Algorithms: Deep Learning based Recommender System: A Survey and New Perspectives (2019)
• Algorithms: Are We Really Making Progress? An Analysis of Neural Recommendation Approaches (2019)
• Serendipity: A Survey of Serendipity in Recommender Systems (2016)
• Diversity: Diversity in Recommender Systems – A survey (2017)
• Explanations: A Survey of Explanations in Recommender Systems (2007)

Deep Learning

• Architecture: A State-of-the-Art Survey on Deep Learning Theory and Architectures (2019)
• Knowledge distillation: Knowledge Distillation: A Survey (2021)
• Model compression: Compression of Deep Learning Models for Text: A Survey (2020)
• Transfer learning: A Survey on Deep Transfer Learning (2018)
• Neural architecture search: A Comprehensive Survey of Neural Architecture Search (2021)
• Neural architecture search: Neural Architecture Search: A Survey (2019)

Natural Language Processing

• Deep Learning: Recent Trends in Deep Learning Based Natural Language Processing (2018)
• Classification: Deep Learning Based Text Classification: A Comprehensive Review (2021)
• Generation: Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation (2018)
• Generation: Neural Language Generation: Formulation, Methods, and Evaluation (2020)
• Transfer learning: Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer (2020)
• Transformers: Efficient Transformers: A Survey (2020)
• Metrics: Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (2020)
• Metrics: Evaluation of Text Generation: A Survey (2020)

Computer Vision

• Object detection: Object Detection in 20 Years (2019)
• Adversarial attacks: Threat of Adversarial Attacks on Deep Learning in Computer Vision (2018)
• Autonomous vehicles: Computer Vision for Autonomous Vehicles: Problems, Datasets and SOTA (2021)
• Image Captioning: A Comprehensive Survey of Deep Learning for Image Captioning (2018)
• Instance Segmentation: A Survey on Instance Segmentation: State of the art
• Vision Transformer: A Survey on Vision Transformer
• Architectures: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
• Transformers: Transformers in Vision: A Survey

Vision and Language

• Trends: Trends in Integration of Vision and Language Research: Tasks, Datasets, and Methods (2021)
• Trends: Multimodal Research in Vision and Language: Current and Emerging Trends (2020)

Reinforcement Learning

• Algorithms: A Brief Survey of Deep Reinforcement Learning (2017)
• Transfer learning: Transfer Learning for Reinforcement Learning Domains (2009)
• Economics: Review of Deep Reinforcement Learning Methods and Applications in Economics (2020)
• Discovery: Deep Reinforcement Learning for Search, Recommendation, and Online Advertising (2018)

Graph

• Survey: A Comprehensive Survey on Graph Neural Networks (2019)
• Survey: A Practical Guide to Graph Neural Networks (2020)
• Fraud detection: A systematic literature review of graph-based anomaly detection approaches (2020)
• Knowledge graphs: A Comprehensive Introduction to Knowledge Graphs (2021)

Embeddings

• Text: From Word to Sense Embeddings:A Survey on Vector Representations of Meaning (2018)
• Text: Diachronic Word Embeddings and Semantic Shifts (2018)
• Text: Word Embeddings: A Survey (2019)
• Text: A Reproducible Survey on Word Embeddings and Ontology-based Methods for Word Similarity (2019)
• Graph: A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications (2017)

Meta-learning and Few-shot Learning

• NLP: Meta-learning for Few-shot Natural Language Processing: A Survey (2020)
• Domain Agnostic: Learning from Few Samples: A Survey (2020)
• Neural Networks: Meta-Learning in Neural Networks: A Survey (2020)
• Domain Agnostic: A Comprehensive Overview and Survey of Recent Advances in Meta-Learning (2020)
• Domain Agnostic: Baby steps towards few-shot learning with multiple semantics (2020)
• Domain Agnostic: Meta-Learning: A Survey (2018)
• Domain Agnostic: A Perspective View And Survey Of Meta-learning (2002)

Others

• Transfer learning: A Survey on Transfer Learning (2009)