{"id":13408623,"url":"https://github.com/markdtw/awesome-architecture-search","last_synced_at":"2025-09-11T21:47:48.133Z","repository":{"id":93067250,"uuid":"112202172","full_name":"markdtw/awesome-architecture-search","owner":"markdtw","description":"A curated list of awesome architecture search resources","archived":false,"fork":false,"pushed_at":"2020-09-15T19:52:12.000Z","size":56,"stargazers_count":1189,"open_issues_count":2,"forks_count":198,"subscribers_count":70,"default_branch":"master","last_synced_at":"2025-08-28T20:37:24.090Z","etag":null,"topics":["deep-learning","neural-architecture-search"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/markdtw.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-11-27T13:52:07.000Z","updated_at":"2025-08-28T10:39:33.000Z","dependencies_parsed_at":"2023-06-04T15:15:05.678Z","dependency_job_id":null,"html_url":"https://github.com/markdtw/awesome-architecture-search","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/markdtw/awesome-architecture-search","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markdtw%2Fawesome-architecture-search","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markdtw%2Fawesome-architecture-search/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markdtw%2Fawesome-architecture-search/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markdtw%2Fawesome-architecture-search/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/markdtw","download_url":"https://codeload.github.com/markdtw/awesome-architecture-search/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markdtw%2Fawesome-architecture-search/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274712275,"owners_count":25335919,"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","status":"online","status_checked_at":"2025-09-11T02:00:13.660Z","response_time":74,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["deep-learning","neural-architecture-search"],"created_at":"2024-07-30T20:00:54.129Z","updated_at":"2025-09-11T21:47:48.083Z","avatar_url":"https://github.com/markdtw.png","language":null,"funding_links":[],"categories":["Related Resources","Uncategorized","Others","Table of Contents","Machine Learning","神经网络结构搜索_Neural_Architecture_Search","Other Lists","AutoML","Core Machine Learning Research"],"sub_categories":["Gradient-based Optimization","Uncategorized","JavaScript","TeX Lists","General ML, Surveys, and Methods"],"readme":"# Awesome Architecture Search [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"250\" src=\"https://camo.githubusercontent.com/1131548cf666e1150ebd2a52f44776d539f06324/68747470733a2f2f63646e2e7261776769742e636f6d2f73696e647265736f726875732f617765736f6d652f6d61737465722f6d656469612f6c6f676f2e737667\" \"Awesome!\"\u003e\n\u003c/p\u003e\n\nA curated list of awesome architecture search and hyper-parameter optimization resources. Inspired by [awesome-deep-vision](https://github.com/kjw0612/awesome-deep-vision), [awesome-adversarial-machine-learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning) and [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers).\n\nHyper-parameter optimization has always been a popular field in the Machine Learning community, architecture search just emerges as a rising star in recent years. These are some of the awesome resources!\n\n## Table of Contents\n\n- [Architecture Search](#architecture-search)\n  - [Reinforcement Learning](#reinforcement-learning)\n  - [Evolutionary Algorithm](#evolutionary-algorithm)\n  - [Others](#others)\n- [Hyper-Parameter Search](#hyper-parameter-search)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Architecture Search\n\n### Reinforcement Learning\n- Neural Architecture Search with Reinforcement Learning [[pdf]](https://arxiv.org/abs/1611.01578)\n  - Barret Zoph and Quoc V. Le. *ICLR'17*\n- Designing Neural Network Architectures Using Reinforcement Learning [[pdf]](https://arxiv.org/abs/1611.02167) [[code]](https://github.com/bowenbaker/metaqnn)\n  - Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar. *ICLR'17*\n- Efficient Architecture Search by Network Transformation [[pdf]](https://arxiv.org/abs/1707.04873) [[code]](https://github.com/han-cai/EAS)\n  - Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, Jun Wang. *AAAI'18*\n- Learning Transferable Architectures for Scalable Image Recognition [[pdf]](https://arxiv.org/abs/1707.07012) [[nasnet]](https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet)\n  - Barret Zoph, Vijay Vasudevan, Jonathan Shlens, Quoc V. Le. *Arxiv 1707*\n- Practical Block-wise Neural Network Architecture Generation [[pdf]](https://arxiv.org/abs/1708.05552)\n  - Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu. *CVPR'18*\n- A Flexible Approach to Automated RNN Architecture Generation [[pdf]](https://arxiv.org/abs/1712.07316)\n  - Martin Schrimpf, Stephen Merity, James Bradbury, Richard Socher. *ICLR'18*\n- Efficient Neural Architecture Search via Parameter Sharing [[pdf]](https://arxiv.org/abs/1802.03268) [[code (not official)]](https://github.com/carpedm20/ENAS-pytorch) [[code (official)]](https://github.com/melodyguan/enas)\n  - Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean. *Arxiv 1802*\n- Path-Level Network Transformation for Efficient Architecture Search [[pdf]](https://arxiv.org/abs/1806.02639) [[code]](https://github.com/han-cai/PathLevel-EAS)\n  - Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu. *ICML'18*\n\n### Evolutionary Algorithm\n- Large-Scale Evolution of Image Classifiers [[pdf]](https://arxiv.org/abs/1703.01041)\n  - Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin. *ICML'17*\n- Genetic CNN [[pdf]](https://arxiv.org/abs/1703.01513) [[code]](https://github.com/aqibsaeed/Genetic-CNN)\n  - Lingxi Xie and Alan Yuille. *ICCV'17*\n- Hierarchical Representations for Efficient Architecture Search [[pdf]](https://arxiv.org/abs/1711.00436)\n  - Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu. *ICLR'18*\n- Regularized Evolution for Image Classifier Architecture Search [[pdf]](https://arxiv.org/abs/1802.01548)\n  - Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V Le. *Arxiv 1802*\n- Weight Agnostic Neural Networks [[pdf]](https://arxiv.org/abs/1906.04358)\n  - Adam Gaier, David Ha. *NeurIPS'19*\n\n### Others\n- Neural Architecture Optimization [[pdf]](https://arxiv.org/abs/1808.07233) [[code]](https://github.com/renqianluo/NAO)\n  - Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu. *Arxiv 1808*\n- DeepArchitect: Automatically Designing and Training Deep Architectures [[pdf]](https://arxiv.org/abs/1704.08792) [[code]](https://github.com/negrinho/deep_architect)\n  - Renato Negrinho and Geoff Gordon. *Arxiv 1704*\n- SMASH: One-Shot Model Architecture Search through HyperNetworks [[pdf]](https://arxiv.org/abs/1708.05344) [[code]](https://github.com/ajbrock/SMASH)\n  - Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston. *ICLR'18*\n- Simple and efficient architecture search for Convolutional Neural Networks [[pdf]](https://arxiv.org/abs/1711.04528)\n  - Thomas Elsken, Jan-Hendrik Metzen, Frank Hutter. *ICLR'18 Workshop*\n- Progressive Neural Architecture Search [[pdf]](https://arxiv.org/abs/1712.00559)\n  - Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy. *Arxiv 1712*\n- DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures [[pdf]](https://arxiv.org/abs/1806.08198)\n  - [Jin-Dong Dong](https://markdtw.github.io), An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun. *ECCV'18*\n- Neural Architecture Search with Bayesian Optimisation and Optimal Transport [[pdf]](https://arxiv.org/abs/1802.07191)\n  - Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric Xing. *Arxiv 1802*\n- Effective Building Block Design for Deep Convolutional Neural Networks using Search [[pdf]](https://arxiv.org/abs/1801.08577)\n  - Jayanta K Dutta, Jiayi Liu, Unmesh Kurup, Mohak Shah. *Arxiv 1801*\n- DARTS: Differentiable Architecture Search [[pdf]](https://arxiv.org/abs/1806.09055) [[code]](https://github.com/quark0/darts)\n  - Hanxiao Liu, Karen Simonyan, Yiming Yang. *Arxiv 1806*\n- Efficient Neural Architecture Search with Network Morphism [[pdf]](https://arxiv.org/abs/1806.10282) [[code]](https://github.com/jhfjhfj1/autokeras)\n  - Haifeng Jin, Qingquan Song, Xia Hu. *Arxiv 1806*\n- Searching for Efficient Multi-Scale Architectures for Dense Image Prediction [[pdf]](https://arxiv.org/abs/1809.04184) \n  - Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens. *Arxiv 1809*\n- AMC: AutoML for Model Compression and Acceleration on Mobile Devices [[pdf]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yihui_He_AMC_Automated_Model_ECCV_2018_paper.pdf) [[code (not official)]](https://github.com/Tencent/PocketFlow)\n  - Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han. *ECCV'18*\n- MorphNet: Fast \u0026 Simple Resource-Constrained Structure Learning of Deep Networks [[pdf]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Gordon_MorphNet_Fast__CVPR_2018_paper.pdf)\n  - Ariel Gordon, Elad Eban, Bo Chen, Ofir Nachum, Tien-Ju Yang, Edward Choi. *CVPR'18*\n- Weight Agnostic Neural Networks [[pdf]](https://arxiv.org/abs/1906.04358)\n  - Adam Gaier, David Ha. *NeurIPS'19*\n- Towards Modular and Programmable Architecture Search [[pdf]](https://arxiv.org/abs/1909.13404) [[code]](https://github.com/negrinho/deep_architect)\n  - Renato Negrinho, Darshan Patil, Nghia Le, Daniel Ferreira, Matthew Gormley, Geoffrey Gordon. *NeurIPS'19*\n\n## Hyper-Parameter Search\n- Speeding up Automatic Hyperparameter Optimization of Deep Neural Networksby Extrapolation of Learning Curves [[pdf]](http://ml.informatik.uni-freiburg.de/papers/15-IJCAI-Extrapolation_of_Learning_Curves.pdf) [[code]](https://github.com/automl/pylearningcurvepredictor)\n  - Tobias Domhan, Jost Tobias Springenberg, Frank Hutter. *IJCAI'15*\n- Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization [[pdf]](https://arxiv.org/abs/1603.06560)\n  - Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar. *ICLR'17*\n- Learning Curve Prediction with Bayesian Neural Networks [[pdf]](http://ml.informatik.uni-freiburg.de/papers/17-ICLR-LCNet.pdf)\n  - Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter. *ICLR'17*\n- Accelerating Neural Architecture Search using Performance Prediction [[pdf]](https://arxiv.org/abs/1705.10823)\n  - Bowen Baker, Otkrist Gupta, Ramesh Raskar, Nikhil Naik. *ICLR'18 Workshop*\n- Hyperparameter Optimization: A Spectral Approach [[pdf]](https://arxiv.org/abs/1706.00764) [[code]](https://github.com/callowbird/Harmonica)\n  - Elad Hazan, Adam Klivans, Yang Yuan. *NIPS DLTP Workshop 2017*\n- Population Based Training of Neural Networks [[pdf]](https://arxiv.org/abs/1711.09846)\n  - Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, Koray Kavukcuoglu. *Arxiv 1711*\n\n\n## Contributing\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"http://cdn1.sportngin.com/attachments/news_article/7269/5172/needyou_small.jpg\" alt=\"We Need You!\"\u003e\n\u003c/p\u003e\n\nPlease help contribute this list by contacting [me](https://markdtw.github.io/) or add [pull request](https://github.com/markdtw/awesome-architecture-search/pulls)\n\nMarkdown format:\n```markdown\n- Paper Name [[pdf]](link) [[code]](link)\n  - Author 1, Author 2, Author 3. *Conference'Year*\n```\n\n\n## License\n\n[![PDM](https://licensebuttons.net/p/mark/1.0/88x31.png)](https://creativecommons.org/publicdomain/zero/1.0/)\n\nTo the extent possible under law, [Mark Dong](https://markdtw.github.io/) has waived all copyright and related or neighboring rights to this work.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkdtw%2Fawesome-architecture-search","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarkdtw%2Fawesome-architecture-search","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkdtw%2Fawesome-architecture-search/lists"}