# awesome-AutoML [](https://awesome.re)
A curated list of Meta-Learning resources. Inspired by [awesome-meta-learning](https://github.com/dragen1860/awesome-meta-learning), [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).
Please feel free to [pull requests](https://github.com/dragen1860/awesome-AutoML/pulls) or [open an issue](https://github.com/dragen1860/awesome-AutoML/issues) to add papers.
# Papers and Code
* [Exploring Randomly Wired Neural Networks for Image Recognition](https://arxiv.org/abs/1904.01569). Saining Xie, Alexander Kirillov, Ross Girshick, Kaiming He.
* [Neural Architecture Optimization](https://arxiv.org/abs/1808.07233). Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu.[](https://github.com/renqianluo/NAO)
* [Probabilistic Neural Architecture Search](https://arxiv.org/abs/1902.05116). Francesco Paolo Casale, Jonathan Gordon, Nicolo Fusi.
* [Fast Task-Aware Architecture Inference](https://arxiv.org/abs/1902.05781). Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, Jesse Berent.
* [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://arxiv.org/abs/1902.09635). Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter.
* [Evaluating the Search Phase of Neural Architecture Search](https://arxiv.org/abs/1902.08142).Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, Mathieu Salzmann.
* [Random Search and Reproducibility for Neural Architecture Search](https://arxiv.org/abs/1902.07638) Liam Li, Ameet Talwalkar
[](https://github.com/liamcli/randomNAS_release)
* [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://hanlab.mit.edu/projects/proxylessNAS/). Han Cai, Ligeng Zhu, Song Han.
[PyTorch](https://github.com/MIT-HAN-LAB/ProxylessNAS)
* [SMASH: One-Shot Model Architecture Search through HyperNetworks](https://arxiv.org/abs/1708.05344).Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston.
* [Understanding and Simplifying One-Shot Architecture Search](http://proceedings.mlr.press/v80/bender18a.html). Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc Le
* [SNAS: Stochastic Neural Architecture Search](https://arxiv.org/abs/1812.09926). Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin. ICLR2019.
* [Efficient Architecture Search by Network Transformation](https://arxiv.org/abs/1707.04873). Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, Jun Wang.
* [Designing Neural Network Architectures using Reinforcement Learning](https://arxiv.org/abs/1611.02167). Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar.
[Caffe](https://github.com/bowenbaker/metaqnn)
* [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012). Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le.
[TF](https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet)
[PyTorch](https://github.com/wandering007/nasnet-pytorch)
* [Progressive Neural Architecture Search](https://arxiv.org/abs/1712.00559). Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy.
[Keras](https://github.com/titu1994/progressive-neural-architecture-search)
[TF](https://github.com/chenxi116/PNASNet.TF)
* [Efficient Neural Architecture Search via Parameter Sharing](https://arxiv.org/abs/1802.03268). Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean.
[TF](https://github.com/melodyguan/enas) [PyTorch](https://github.com/carpedm20/ENAS-pytorch)
* [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055). Hanxiao Liu, Karen Simonyan, Yiming Yang.
[Pytorch](https://github.com/quark0/darts)
* [Practical Block-wise Neural Network Architecture Generation](https://arxiv.org/abs/1708.05552). Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu.
* [Large-Scale Evolution of Image Classifiers](https://arxiv.org/abs/1703.01041). Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin.
* [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578). Barret Zoph, Quoc V. Le.
# Tutorials and slides
* [NeurIPS2018: Automatic Machine Learning](https://www.facebook.com/nipsfoundation/videos/199543964204829/).
* [PNAS @ ECCV](https://cs.jhu.edu/~cxliu/slides/pnas-talk-eccv.pdf).
* [What’s the deal with Neural Architecture Search?](https://determined.ai/blog/neural-architecture-search)
# Researchers and Labs
* [Liam Li](https://liamcli.com/), CMU.
* [Barret Zoph](http://barretzoph.github.io/), Google Brain.
* [Chenxi Liu](http://www.cs.jhu.edu/~cxliu/). Johns Hopkins University.
* [Bowen Baker](https://bowenbaker.github.io/). OpenAI.
* [Hanxiao Liu](http://www.cs.cmu.edu/~hanxiaol/). CMU.
* [Song Han](https://songhan.mit.edu/). MIT.
# Mixed resources
* [automl.org](https://www.automl.org/)
* [Yet Another Good List](https://www.automl.org/automl/literature-on-neural-architecture-search/)
