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Decision brief
ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems.
Good fit when
- When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning
- For researchers or practitioners looking to quickly catch up on recent developments across various subfields of AI without reading each original paper
Avoid when
- If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary
- In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
Observed Jul 12, 2026 · Source: enrich:decision_facts
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git clone https://github.com/eugeneyan/ml-surveysSimilar tools
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Overview
A collection of survey papers that cover advancements and research in deep learning, natural language processing, computer vision, graphs, reinforcement learning, recommendations, among other areas within the field of artificial intelligence.
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README
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 👉applied-ml
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 Se
For agents
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