{"data":{"slug":"d-x-y-awesome-autodl","name":"Awesome-AutoDL","tagline":"Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)","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":null,"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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