{"id":13409440,"url":"https://github.com/eugeneyan/ml-surveys","last_synced_at":"2025-03-23T09:21:18.708Z","repository":{"id":39985701,"uuid":"285951873","full_name":"eugeneyan/ml-surveys","owner":"eugeneyan","description":"📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.","archived":false,"fork":false,"pushed_at":"2023-03-17T05:00:49.000Z","size":20,"stargazers_count":2838,"open_issues_count":2,"forks_count":290,"subscribers_count":155,"default_branch":"main","last_synced_at":"2025-01-28T15:49:30.058Z","etag":null,"topics":["computer-vision","deep-learning","embeddings","machine-learning","nlp","recommender-system","reinforcement-learning","survey"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/eugeneyan.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-08-08T01:35:06.000Z","updated_at":"2025-01-23T11:32:27.000Z","dependencies_parsed_at":"2022-08-09T15:48:13.316Z","dependency_job_id":"672131f3-fb61-4736-b0bb-9ee2dc831927","html_url":"https://github.com/eugeneyan/ml-surveys","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eugeneyan%2Fml-surveys","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eugeneyan%2Fml-surveys/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eugeneyan%2Fml-surveys/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eugeneyan%2Fml-surveys/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/eugeneyan","download_url":"https://codeload.github.com/eugeneyan/ml-surveys/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245079452,"owners_count":20557477,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","deep-learning","embeddings","machine-learning","nlp","recommender-system","reinforcement-learning","survey"],"created_at":"2024-07-30T20:01:00.814Z","updated_at":"2025-03-23T09:21:18.683Z","avatar_url":"https://github.com/eugeneyan.png","language":null,"funding_links":[],"categories":["Others",":octocat: GitHub Repositories","Reinforcement Learning","Core Machine Learning Research"],"sub_categories":["Ukraine","Inverse Reinforcement Learning","General ML, Surveys, and Methods"],"readme":"# ml-surveys\n\nIt'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**.\n\n[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](./CONTRIBUTING.md)\n\nFiguring out how to implement your ML project? Learn how other organizations did it 👉[`applied-ml`](https://github.com/eugeneyan/applied-ml)\n\n**Table of Contents**\n\n- [Recommendation](#recommendation)\n- [Deep Learning](#deep-learning)\n- [Natural Language Processing](#natural-language-processing)\n- [Computer Vision](#computer-vision)\n- [Vision and Language](#vision-and-language)\n- [Reinforcement Learning](#reinforcement-learning)\n- [Graph](#graph)\n- [Embeddings](#embeddings)\n- [Meta-learning and Few-shot Learning](#meta-learning-and-few-shot-Learning)\n- [Others](#others)\n\n## Recommendation\n- Algorithms: [Recommender systems survey (2013)](http://irntez.ir/wp-content/uploads/2016/12/sciencedirec.pdf)\n- Algorithms: [Deep Learning based Recommender System: A Survey and New Perspectives (2019)](https://arxiv.org/pdf/1707.07435.pdf)\n- Algorithms: [Are We Really Making Progress? An Analysis of Neural Recommendation Approaches (2019)](https://arxiv.org/pdf/1907.06902.pdf)\n- Serendipity: [A Survey of Serendipity in Recommender Systems (2016)](https://www.researchgate.net/publication/306075233_A_Survey_of_Serendipity_in_Recommender_Systems)\n- Diversity: [Diversity in Recommender Systems – A survey (2017)](https://papers-gamma.link/static/memory/pdfs/153-Kunaver_Diversity_in_Recommender_Systems_2017.pdf)\n- Explanations: [A Survey of Explanations in Recommender Systems (2007)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.418.9237\u0026rep=rep1\u0026type=pdf)\n\n## Deep Learning\n- Architecture: [A State-of-the-Art Survey on Deep Learning Theory and Architectures (2019)](https://www.mdpi.com/2079-9292/8/3/292/htm)\n- Knowledge distillation: [Knowledge Distillation: A Survey (2021)](https://arxiv.org/pdf/2006.05525.pdf)\n- Model compression: [Compression of Deep Learning Models for Text: A Survey (2020)](https://arxiv.org/pdf/2008.05221.pdf)\n- Transfer learning: [A Survey on Deep Transfer Learning (2018)](https://arxiv.org/pdf/1808.01974.pdf)\n- Neural architecture search: [A Comprehensive Survey of Neural Architecture Search (2021)](https://arxiv.org/abs/2006.02903)\n- Neural architecture search: [Neural Architecture Search: A Survey (2019)](https://arxiv.org/abs/1808.05377)\n\n## Natural Language Processing\n- Deep Learning: [Recent Trends in Deep Learning Based Natural Language Processing (2018)](https://arxiv.org/pdf/1708.02709.pdf)\n- Classification: [Deep Learning Based Text Classification: A Comprehensive Review (2021)](https://arxiv.org/pdf/2004.03705)\n- 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)\n- Generation: [Neural Language Generation: Formulation, Methods, and Evaluation (2020)](https://arxiv.org/pdf/2007.15780.pdf)\n- Transfer learning: [Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer (2020)](https://arxiv.org/abs/1910.10683)\n- Transformers: [Efficient Transformers: A Survey (2020)](https://arxiv.org/pdf/2009.06732.pdf)\n- Metrics: [Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (2020)](https://arxiv.org/pdf/2005.04118.pdf)\n- Metrics: [Evaluation of Text Generation: A Survey (2020)](https://arxiv.org/pdf/2006.14799.pdf)\n\n## Computer Vision\n- Object detection: [Object Detection in 20 Years (2019)](https://arxiv.org/pdf/1905.05055.pdf)\n- Adversarial attacks: [Threat of Adversarial Attacks on Deep Learning in Computer Vision (2018)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8294186)\n- Autonomous vehicles: [Computer Vision for Autonomous Vehicles: Problems, Datasets and SOTA (2021)](https://arxiv.org/pdf/1704.05519.pdf)\n- Image Captioning: [A Comprehensive Survey of Deep Learning for Image Captioning (2018)](https://arxiv.org/pdf/1810.04020.pdf)\n- Instance Segmentation: [A Survey on Instance Segmentation: State of the art](https://arxiv.org/abs/2007.00047)\n- Vision Transformer: [A Survey on Vision Transformer](https://arxiv.org/abs/2012.12556)\n- Architectures: [Review of deep learning: concepts, CNN architectures, challenges, applications, future directions](https://link.springer.com/article/10.1186/s40537-021-00444-8)\n- Transformers: [Transformers in Vision: A Survey](https://arxiv.org/abs/2101.01169)\n\n## Vision and Language\n\n- Trends: [Trends in Integration of Vision and Language Research: Tasks, Datasets, and Methods (2021)](https://doi.org/10.1613/jair.1.11688) \n- Trends: [Multimodal Research in Vision and Language: Current and Emerging Trends (2020)](https://arxiv.org/abs/2010.09522) \n\n## Reinforcement Learning\n- Algorithms: [A Brief Survey of Deep Reinforcement Learning (2017)](https://arxiv.org/pdf/1708.05866.pdf)\n- Transfer learning: [Transfer Learning for Reinforcement Learning Domains (2009)](http://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf)\n- Economics: [Review of Deep Reinforcement Learning Methods and Applications in Economics (2020)](https://arxiv.org/pdf/2004.01509.pdf)\n- Discovery: [Deep Reinforcement Learning for Search, Recommendation, and Online Advertising (2018)](https://arxiv.org/pdf/1812.07127.pdf)\n\n## Graph\n- Survey: [A Comprehensive Survey on Graph Neural Networks (2019)](https://arxiv.org/pdf/1901.00596.pdf)\n- Survey: [A Practical Guide to Graph Neural Networks (2020)](https://arxiv.org/pdf/2010.05234.pdf)\n- Fraud detection: [A systematic literature review of graph-based anomaly detection approaches (2020)](https://www.sciencedirect.com/science/article/pii/S0167923620300580)\n- Knowledge graphs: [A Comprehensive Introduction to Knowledge Graphs (2021)](https://arxiv.org/pdf/2003.02320.pdf)\n\n## Embeddings\n- Text: [From Word to Sense Embeddings:A Survey on Vector Representations of Meaning (2018)](https://www.jair.org/index.php/jair/article/view/11259/26454)\n- Text: [Diachronic Word Embeddings and Semantic Shifts (2018)](https://arxiv.org/pdf/1806.03537.pdf)\n- Text: [Word Embeddings: A Survey (2019)](https://arxiv.org/abs/1901.09069)\n- Text: [A Reproducible Survey on Word Embeddings and Ontology-based Methods for Word Similarity (2019)](https://doi.org/10.1016/j.engappai.2019.07.010)\n- Graph: [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications (2017)](https://arxiv.org/pdf/1709.07604)\n\n## Meta-learning and Few-shot Learning\n- NLP: [Meta-learning for Few-shot Natural Language Processing: A Survey (2020)](https://arxiv.org/abs/2007.09604)\n- Domain Agnostic: [Learning from Few Samples: A Survey (2020)](https://arxiv.org/abs/2007.15484)\n- Neural Networks: [Meta-Learning in Neural Networks: A Survey (2020)](https://arxiv.org/abs/2004.05439)\n- Domain Agnostic: [A Comprehensive Overview and Survey of Recent Advances in Meta-Learning (2020)](https://arxiv.org/abs/2004.11149)\n- Domain Agnostic: [Baby steps towards few-shot learning with multiple semantics (2020)](https://arxiv.org/abs/1906.01905)\n- Domain Agnostic: [Meta-Learning: A Survey (2018)](https://arxiv.org/abs/1810.03548)\n- Domain Agnostic: [A Perspective View And Survey Of Meta-learning (2002)](https://www.researchgate.net/publication/2375370_A_Perspective_View_And_Survey_Of_Meta-Learning)\n\n## Others\n- Transfer learning: [A Survey on Transfer Learning (2009)](https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feugeneyan%2Fml-surveys","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feugeneyan%2Fml-surveys","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feugeneyan%2Fml-surveys/lists"}