Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
| Date | Stars |
|---|---|
| 2026-07-24 | 2902 |
| 2026-07-25 | 2902 |
| 2026-07-28 | 2903 |
| 2026-07-30 | 2903 |
| 2026-07-31 | 2904 |
| 2026-08-04 | 2905 |
| 2026-08-05 | 2905 |
| 2026-08-06 | 2905 |
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growth rate 0.07%/day
# 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**. [](./CONTRIBUTING.md) Figuring out how to implement your ML project? Learn how other organizations did it 👉[`applied-ml`](https://github.com/eugeneyan/applied-ml) **Table of Contents** - [Recommendation](#recommendation) - [Deep Learning](#deep-learning) - [Natural Language Processing](#natural-language-processing) - [Computer Vision](#computer-vision) - [Vision and Language](#vision-and-language) - [Reinforcement Learning](#reinforcement-learning) - [Graph](#graph) - [Embeddings](#embeddings) - [Meta-learning and Few-shot Learning](#meta-learning-and-few-shot-Learning) - [Others](#others) ## Recommendation - Algorithms: [Recommender systems survey (2013)](http://irntez.ir/wp-content/uploads/2016/12/sciencedirec.pdf) - Algorithms: [Deep Learning based Recommender System: A Survey and New Perspectives (2019)](https://arxiv.org/pdf/1707.07435.pdf) - Algorithms: [Are We Really Making Progress? An Analysis of Neural Recommendation Approaches (2019)](https://arxiv.org/pdf/1907.06902.pdf) - Serendipity: [A Survey of Serendipity in Recommender Systems (2016)](https://www.researchgate.net/publication/306075233_A_Survey_of_Serendipity_in_Recommender_Systems) - Diversity: [Diversity in Recommender Systems – A survey (2017)](https://papers-gamma.link/static/memory/pdfs/153-Kunaver_Diversity_in_Recommender_Systems_2017.pdf) - Explanations: [A Survey of Explanations in Recommender Systems (2007)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.418.9237&rep=rep1&type=pdf) ## Deep Learning - Architecture: [A State-of-the-Art Survey on Deep Learning Theory and Architectures (2019)](https://www.mdpi.com/2079-9292/8/3/292/htm) - Knowledge distillation: [Knowledge Distillation: A Survey (2021)](https://arxiv.org/pdf/2006.05525.pdf) - Model compression: [Compression of Deep Learning Models for Text: A Survey (2020)](https://arxiv.org/pdf/2008.05221.pdf) - Transfer learning: [A Survey on Deep Transfer Learning (2018)](https://arxiv.org/pdf/1808.01974.pdf) - Neural architecture search: [A Comprehensive Survey of Neural Architecture Search (2021)](https://arxiv.org/abs/2006.02903) - Neural architecture search: [Neural Architecture Search: A Survey (2019)](https://arxiv.org/abs/1808.05377) ## Natural Language Processing - Deep Learning: [Recent Trends in Deep Learning Based Natural Language Processing (2018)](https://arxiv.org/pdf/1708.02709.pdf) - Classification: [Deep Learning Based Text Classification: A Comprehensive Review (2021)](https://arxiv.org/pdf/2004.03705) - Generation: [Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation (2018)](https://www.jair.org/index.php/jair/article/view/11173/26378) - Generation: [Neural Language Generation: Formulation, Methods, and Evaluation (2020)](https://arxiv.org/pdf/2007.15780.pdf) - Transfer learning: [Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer (2020)](https://arxiv.org/abs/1910.10683) - Transformers: [Efficient Transformers: A Survey (2020)](https://arxiv.org/pdf/2009.06732.pdf) - Metrics: [Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (2020)](https://arxiv.org/pdf/2005.04118.pdf) - Metrics: [Evaluation of Text Generation: A Survey (2020)](https://arxiv.org/pdf/2006.14799.pdf) ## Computer Vision - Object detection: [Object Detection in 20 Years (2019)](https://arxiv.org/pdf/1905.05055.pdf) - Adversarial attacks: [Threat of Adversarial Attacks on Deep Learning in Computer Vision (2018)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8294186) - Autonomous vehicles: [Computer Vision for Autonomous Vehicles: Problems, Datasets and SOTA (2021)](https://arxiv.org/pdf/1704.05519.pdf) - Image Captioning: [A Comprehensive Survey of Deep Le
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Read on GitHub
Eugene Yan · @anthropics · United States
32
Mario · Universität Stuttgart · Germany
3
Juan J. Lastra-Díaz · Universidad Nacional de Educación a Distancia (UNED) · Spain
2
Felipe Almeida (queirozfcom) · @nubank
1
Sung Kim · HKUST · Hong Kong
1
1
Dimitrios Gagatsis · TomTom · Netherlands
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b4b123f563dd074c, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
matched fp:b4b123f563dd074c, topic:deep-learning
matched fp:b4b123f563dd074c, topic:computer-vision, readme:computer vision, readme:object detection
matched fp:b4b123f563dd074c, topic:nlp, readme:natural language processing, readme:text classification