eugeneyan / Ml Surveys
Licence: mit
π Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
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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.
Figuring out how to implement your ML project? Learn how other organizations did it πapplied-ml
Table of Contents
- Recommendation
- Deep Learning
- Natural Language Processing
- Computer Vision
- Reinforcement Learning
- Graph
- 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 β 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
- 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
- Transformers: Efficient Transformers: A Survey
- 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
- Image Captioning: A Comprehensive Survey of Deep Learning for Image Captioning
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
Graph
- Survey: A Comprehensive Survey on Graph Neural Networks
- Survey: A Practical Guide to Graph Neural Networks
- Fraud detection: A systematic literature review of graph-based anomaly detection approaches
- Knowledge graphs: A Comprehensive Introduction to Knowledge Graphs
Embeddings
- 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
- Text: A Reproducible Survey on Word Embeddings and Ontology-based Methods for Word Similarity
- Graph: A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
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
- Neural Networks: 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
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