├── CONTRIBUTING.md ├── LICENSE └── README.md /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | ## Contribution Guide 2 | 3 | - Have a survey you're not seeing here? Want to contribute? Make a [pull request (PR)](https://github.com/eugeneyan/ml-surveys/pulls)! 😄 4 | - New to [Markdown](https://www.markdownguide.org/cheat-sheet/)? Here's how it would look like: 5 | 6 | ``` 7 | - Topic: [Title of Paper, Article, Blog](url) 8 | ``` 9 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Eugene Yan 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ml-surveys 2 | 3 | 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**. 4 | 5 | [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](./CONTRIBUTING.md) 6 | 7 | Figuring out how to implement your ML project? Learn how other organizations did it 👉[`applied-ml`](https://github.com/eugeneyan/applied-ml) 8 | 9 | **Table of Contents** 10 | 11 | - [Recommendation](#recommendation) 12 | - [Deep Learning](#deep-learning) 13 | - [Natural Language Processing](#natural-language-processing) 14 | - [Computer Vision](#computer-vision) 15 | - [Vision and Language](#vision-and-language) 16 | - [Reinforcement Learning](#reinforcement-learning) 17 | - [Graph](#graph) 18 | - [Embeddings](#embeddings) 19 | - [Meta-learning and Few-shot Learning](#meta-learning-and-few-shot-Learning) 20 | - [Others](#others) 21 | 22 | ## Recommendation 23 | - Algorithms: [Recommender systems survey (2013)](http://irntez.ir/wp-content/uploads/2016/12/sciencedirec.pdf) 24 | - Algorithms: [Deep Learning based Recommender System: A Survey and New Perspectives (2019)](https://arxiv.org/pdf/1707.07435.pdf) 25 | - Algorithms: [Are We Really Making Progress? An Analysis of Neural Recommendation Approaches (2019)](https://arxiv.org/pdf/1907.06902.pdf) 26 | - Serendipity: [A Survey of Serendipity in Recommender Systems (2016)](https://www.researchgate.net/publication/306075233_A_Survey_of_Serendipity_in_Recommender_Systems) 27 | - Diversity: [Diversity in Recommender Systems – A survey (2017)](https://papers-gamma.link/static/memory/pdfs/153-Kunaver_Diversity_in_Recommender_Systems_2017.pdf) 28 | - 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) 29 | 30 | ## Deep Learning 31 | - Architecture: [A State-of-the-Art Survey on Deep Learning Theory and Architectures (2019)](https://www.mdpi.com/2079-9292/8/3/292/htm) 32 | - Knowledge distillation: [Knowledge Distillation: A Survey (2021)](https://arxiv.org/pdf/2006.05525.pdf) 33 | - Model compression: [Compression of Deep Learning Models for Text: A Survey (2020)](https://arxiv.org/pdf/2008.05221.pdf) 34 | - Transfer learning: [A Survey on Deep Transfer Learning (2018)](https://arxiv.org/pdf/1808.01974.pdf) 35 | - Neural architecture search: [A Comprehensive Survey of Neural Architecture Search (2021)](https://arxiv.org/abs/2006.02903) 36 | - Neural architecture search: [Neural Architecture Search: A Survey (2019)](https://arxiv.org/abs/1808.05377) 37 | 38 | ## Natural Language Processing 39 | - Deep Learning: [Recent Trends in Deep Learning Based Natural Language Processing (2018)](https://arxiv.org/pdf/1708.02709.pdf) 40 | - Classification: [Deep Learning Based Text Classification: A Comprehensive Review (2021)](https://arxiv.org/pdf/2004.03705) 41 | - 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) 42 | - Generation: [Neural Language Generation: Formulation, Methods, and Evaluation (2020)](https://arxiv.org/pdf/2007.15780.pdf) 43 | - Transfer learning: [Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer (2020)](https://arxiv.org/abs/1910.10683) 44 | - Transformers: [Efficient Transformers: A Survey (2020)](https://arxiv.org/pdf/2009.06732.pdf) 45 | - Metrics: [Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (2020)](https://arxiv.org/pdf/2005.04118.pdf) 46 | - Metrics: [Evaluation of Text Generation: A Survey (2020)](https://arxiv.org/pdf/2006.14799.pdf) 47 | 48 | ## Computer Vision 49 | - Object detection: [Object Detection in 20 Years (2019)](https://arxiv.org/pdf/1905.05055.pdf) 50 | - Adversarial attacks: [Threat of Adversarial Attacks on Deep Learning in Computer Vision (2018)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8294186) 51 | - Autonomous vehicles: [Computer Vision for Autonomous Vehicles: Problems, Datasets and SOTA (2021)](https://arxiv.org/pdf/1704.05519.pdf) 52 | - Image Captioning: [A Comprehensive Survey of Deep Learning for Image Captioning (2018)](https://arxiv.org/pdf/1810.04020.pdf) 53 | - Instance Segmentation: [A Survey on Instance Segmentation: State of the art](https://arxiv.org/abs/2007.00047) 54 | - Vision Transformer: [A Survey on Vision Transformer](https://arxiv.org/abs/2012.12556) 55 | - Architectures: [Review of deep learning: concepts, CNN architectures, challenges, applications, future directions](https://link.springer.com/article/10.1186/s40537-021-00444-8) 56 | - Transformers: [Transformers in Vision: A Survey](https://arxiv.org/abs/2101.01169) 57 | 58 | ## Vision and Language 59 | 60 | - Trends: [Trends in Integration of Vision and Language Research: Tasks, Datasets, and Methods (2021)](https://doi.org/10.1613/jair.1.11688) 61 | - Trends: [Multimodal Research in Vision and Language: Current and Emerging Trends (2020)](https://arxiv.org/abs/2010.09522) 62 | 63 | ## Reinforcement Learning 64 | - Algorithms: [A Brief Survey of Deep Reinforcement Learning (2017)](https://arxiv.org/pdf/1708.05866.pdf) 65 | - Transfer learning: [Transfer Learning for Reinforcement Learning Domains (2009)](http://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf) 66 | - Economics: [Review of Deep Reinforcement Learning Methods and Applications in Economics (2020)](https://arxiv.org/pdf/2004.01509.pdf) 67 | - Discovery: [Deep Reinforcement Learning for Search, Recommendation, and Online Advertising (2018)](https://arxiv.org/pdf/1812.07127.pdf) 68 | 69 | ## Graph 70 | - Survey: [A Comprehensive Survey on Graph Neural Networks (2019)](https://arxiv.org/pdf/1901.00596.pdf) 71 | - Survey: [A Practical Guide to Graph Neural Networks (2020)](https://arxiv.org/pdf/2010.05234.pdf) 72 | - Fraud detection: [A systematic literature review of graph-based anomaly detection approaches (2020)](https://www.sciencedirect.com/science/article/pii/S0167923620300580) 73 | - Knowledge graphs: [A Comprehensive Introduction to Knowledge Graphs (2021)](https://arxiv.org/pdf/2003.02320.pdf) 74 | 75 | ## Embeddings 76 | - 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) 77 | - Text: [Diachronic Word Embeddings and Semantic Shifts (2018)](https://arxiv.org/pdf/1806.03537.pdf) 78 | - Text: [Word Embeddings: A Survey (2019)](https://arxiv.org/abs/1901.09069) 79 | - 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) 80 | - Graph: [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications (2017)](https://arxiv.org/pdf/1709.07604) 81 | 82 | ## Meta-learning and Few-shot Learning 83 | - NLP: [Meta-learning for Few-shot Natural Language Processing: A Survey (2020)](https://arxiv.org/abs/2007.09604) 84 | - Domain Agnostic: [Learning from Few Samples: A Survey (2020)](https://arxiv.org/abs/2007.15484) 85 | - Neural Networks: [Meta-Learning in Neural Networks: A Survey (2020)](https://arxiv.org/abs/2004.05439) 86 | - Domain Agnostic: [A Comprehensive Overview and Survey of Recent Advances in Meta-Learning (2020)](https://arxiv.org/abs/2004.11149) 87 | - Domain Agnostic: [Baby steps towards few-shot learning with multiple semantics (2020)](https://arxiv.org/abs/1906.01905) 88 | - Domain Agnostic: [Meta-Learning: A Survey (2018)](https://arxiv.org/abs/1810.03548) 89 | - 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) 90 | 91 | ## Others 92 | - Transfer learning: [A Survey on Transfer Learning (2009)](https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf) 93 | --------------------------------------------------------------------------------