{"data":{"slug":"d-x-y-awesome-autodl","name":"Awesome-AutoDL","tagline":"Curated list of automated deep learning resources covering AutoDL, NAS, HPO","github_url":"https://github.com/D-X-Y/Awesome-AutoDL","owner":"D-X-Y","repo":"Awesome-AutoDL","owner_avatar_url":"https://avatars.githubusercontent.com/u/9547057?v=4","primary_language":"Python","stars":2339,"forks":319,"topics":["autodl","automl","awesome","deep-learning","hyper-parameter-optimization","nas","neural-architecture-search"],"archived":false,"github_pushed_at":"2022-09-26T01:35:49+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/d-x-y-awesome-autodl","markdown_url":"https://www.graphcanon.com/tools/d-x-y-awesome-autodl.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/d-x-y-awesome-autodl","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=d-x-y-awesome-autodl","description":"Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)","homepage_url":null,"license":"MIT","open_issues":2,"watchers":107,"ai_summary":"Provides links to libraries, benchmark information, and surveys related to Automated Deep Learning including neural architecture search and hyper-parameter optimization techniques.","readme_excerpt":"<font size=6><center><big><b> Awesome AutoDL  </b></big></center></font>\n\nA curated list of automated deep learning related 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), [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers), and [awesome-architecture-search](https://github.com/markdtw/awesome-architecture-search).\n\nPlease feel free to [pull requests](https://github.com/D-X-Y/Awesome-AutoDL/pulls) or [open an issue](https://github.com/D-X-Y/Awesome-AutoDL/issues) to add papers.\n\n---\n\n<font size=5><center><b> Table of Contents </b> </center></font>\n\n- [Awesome Blogs](#awesome-blogs)\n- [Awesome AutoDL Libraies](#awesome-autodl-libraies)\n- [Awesome Benchmarks](#awesome-benchmarks)\n- [Deep Learning-based NAS and HPO](#deep-learning-based-nas-and-hpo)\n  - [2021 Venues](#2021-venues)\n  - [2020 Venues](#2020-venues)\n  - [2019 Venues](#2019-venues)\n  - [2018 Venues](#2018-venues)\n  - [2017 Venues](#2017-venues)\n  - [Previous Venues](#previous-venues)\n  - [arXiv](#arxiv)\n- [Awesome Surveys](#awesome-surveys)\n\n---\n\n# Awesome Blogs\n\n- [AutoML info](http://automl.chalearn.org/) and [AutoML Freiburg-Hannover](https://www.automl.org/)\n- [What’s the deal with Neural Architecture Search?](https://determined.ai/blog/neural-architecture-search/)\n- [Google Could AutoML](https://cloud.google.com/vision/automl/docs/beginners-guide) and [PocketFlow](https://pocketflow.github.io/)\n- [AutoML Challenge](http://automl.chalearn.org/) and [AutoDL Challenge](https://autodl.chalearn.org/)\n- [In Defense of Weight-sharing for Neural Architecture Search: an optimization perspective](https://determined.ai/blog/ws-optimization-for-nas/)\n\n# Awesome AutoDL Libraies\n\n- [PyGlove](https://proceedings.neurips.cc/paper/2020/file/012a91467f210472fab4e11359bbfef6-Paper.pdf)\n- [NASLib](https://github.com/automl/NASLib)\n- [Keras Tuner](https://keras-team.github.io/keras-tuner/)\n- [NNI](https://github.com/microsoft/nni)\n- [AutoGluon](https://autogluon.mxnet.io/)\n- [Auto-PyTorch](https://github.com/automl/Auto-PyTorch)\n- [AutoDL-Projects](https://github.com/D-X-Y/AutoDL-Projects)\n- [aw_nas](https://github.com/walkerning/aw_nas)\n- [Determined](https://github.com/determined-ai/determined)\n- [TPOT](https://github.com/EpistasisLab/tpot)\n\n# Awesome Benchmarks\n\n| Title | Venue | Code |\n|:--------|:--------:|:--------:|\n| [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://arxiv.org/pdf/1902.09635.pdf) | ICML 2019 | [GitHub](https://github.com/google-research/nasbench) |\n| [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr) | ICLR 2020 | [Github](https://github.com/D-X-Y/NAS-Bench-201) |\n| [NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search](https://arxiv.org/abs/2008.09777) | arXiv 2020 | [GitHub](https://github.com/automl/nasbench301) |\n| [NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search](https://arxiv.org/abs/2001.10422) | ICLR 2020 | [GitHub](https://github.com/automl/nasbench-1shot1) |\n| [NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size](https://arxiv.org/abs/2009.00437) | TPAMI 2021 | [GitHub](https://github.com/D-X-Y/NATS-Bench)\n| [NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition](https://openreview.net/forum?id=CU0APx9LMaL) | ICLR 2021 | [GitHub](https://github.com/SamsungLabs/nb-asr) |\n| [HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark](https://openreview.net/pdf?id=_0kaDkv3dVf) | ICLR 2021 | [GitHub](https://github.com/RICE-EIC/HW-NAS-Bench) |\n| [NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing](https://arxiv.org/pdf/2006.07116.pdf) | arXiv 2020 | [GitHub](https://github.com/fmsnew/nas-bench-nlp-release) |\n| 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this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS).","You should use Awesome-AutoDL if your research or project benefits from a thorough literature review on recent advancements in Automated Deep Learning technology.","This can be valuable for developers aiming to familiarize themselves with various libraries such as NASLib, AutoGluon, and PyGlove for automated model architecture design."],"when_not_to_use":["Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned.","Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference 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