awesome-AutoML

A curated list of Meta-Learning resources. Inspired by awesome-meta-learning, awesome-adversarial-machine-learning, awesome-deep-learning-papers, and awesome-architecture-search.

Please feel free to pull requests or open an issue to add papers.

Papers and Code

• Exploring Randomly Wired Neural Networks for Image Recognition. Saining Xie, Alexander Kirillov, Ross Girshick, Kaiming He.

• Neural Architecture Optimization. Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu.

• Probabilistic Neural Architecture Search. Francesco Paolo Casale, Jonathan Gordon, Nicolo Fusi.

• Fast Task-Aware Architecture Inference. Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, Jesse Berent.

• NAS-Bench-101: Towards Reproducible Neural Architecture Search. Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter.

• Evaluating the Search Phase of Neural Architecture Search.Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, Mathieu Salzmann.

• Random Search and Reproducibility for Neural Architecture Search Liam Li, Ameet Talwalkar

• ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. Han Cai, Ligeng Zhu, Song Han.

PyTorch

• SMASH: One-Shot Model Architecture Search through HyperNetworks.Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston.

• Understanding and Simplifying One-Shot Architecture Search. Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc Le

• SNAS: Stochastic Neural Architecture Search. Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin. ICLR2019.

• Efficient Architecture Search by Network Transformation. Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, Jun Wang.

• Designing Neural Network Architectures using Reinforcement Learning. Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar. Caffe

• Learning Transferable Architectures for Scalable Image Recognition. Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. TF PyTorch

• Progressive Neural Architecture Search. Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy. Keras TF

• Efficient Neural Architecture Search via Parameter Sharing. Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean. TF PyTorch

• DARTS: Differentiable Architecture Search. Hanxiao Liu, Karen Simonyan, Yiming Yang. Pytorch

• Practical Block-wise Neural Network Architecture Generation. Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu.

• Large-Scale Evolution of Image Classifiers. Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin.

• Neural Architecture Search with Reinforcement Learning. Barret Zoph, Quoc V. Le.

Tutorials and slides

• NeurIPS2018: Automatic Machine Learning.
• PNAS @ ECCV.
• What’s the deal with Neural Architecture Search?

Researchers and Labs

• Liam Li, CMU.
• Barret Zoph, Google Brain.
• Chenxi Liu. Johns Hopkins University.
• Bowen Baker. OpenAI.
• Hanxiao Liu. CMU.
• Song Han. MIT.

Mixed resources

• automl.org
• Yet Another Good List