https://github.com/eric-erki/awesome-architecture-search

A curated list of awesome architecture search resources
https://github.com/eric-erki/awesome-architecture-search

List: awesome-architecture-search

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A curated list of awesome architecture search resources

• Host: GitHub
• URL: https://github.com/eric-erki/awesome-architecture-search
• Owner: eric-erki
• Created: 2020-02-02T15:49:25.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-02T15:50:02.000Z (almost 5 years ago)
• Last Synced: 2024-04-22T02:05:13.434Z (8 months ago)
• Size: 36.1 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• ultimate-awesome - awesome-architecture-search - A curated list of awesome architecture search resources. (Other Lists / PowerShell Lists)

README

        
# Awesome Architecture Search [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



  



A 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).

Hyper-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!

## Table of Contents

- [Architecture Search](#architecture-search)

  - [Reinforcement Learning](#reinforcement-learning)

  - [Evolutionary Algorithm](#evolutionary-algorithm)

  - [Others](#others)

- [Hyper-Parameter Search](#hyper-parameter-search)

- [Contributing](#contributing)

- [License](#license)

## Architecture Search

### Reinforcement Learning

- Neural Architecture Search with Reinforcement Learning [[pdf]](https://arxiv.org/abs/1611.01578)

  - Barret Zoph and Quoc V. Le. *ICLR'17*

- Designing Neural Network Architectures Using Reinforcement Learning [[pdf]](https://arxiv.org/abs/1611.02167) [[code]](https://github.com/bowenbaker/metaqnn)

  - Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar. *ICLR'17*

- Efficient Architecture Search by Network Transformation [[pdf]](https://arxiv.org/abs/1707.04873) [[code]](https://github.com/han-cai/EAS)

  - Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, Jun Wang. *AAAI'18*

- 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)

  - Barret Zoph, Vijay Vasudevan, Jonathan Shlens, Quoc V. Le. *Arxiv 1707*

- Practical Block-wise Neural Network Architecture Generation [[pdf]](https://arxiv.org/abs/1708.05552)

  - Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu. *CVPR'18*

- A Flexible Approach to Automated RNN Architecture Generation [[pdf]](https://arxiv.org/abs/1712.07316)

  - Martin Schrimpf, Stephen Merity, James Bradbury, Richard Socher. *ICLR'18*

- 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)

  - Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean. *Arxiv 1802*

- Path-Level Network Transformation for Efficient Architecture Search [[pdf]](https://arxiv.org/abs/1806.02639) [[code]](https://github.com/han-cai/PathLevel-EAS)

  - Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu. *ICML'18*

### Evolutionary Algorithm

- Large-Scale Evolution of Image Classifiers [[pdf]](https://arxiv.org/abs/1703.01041)

  - Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin. *ICML'17*

- Genetic CNN [[pdf]](https://arxiv.org/abs/1703.01513) [[code]](https://github.com/aqibsaeed/Genetic-CNN)

  - Lingxi Xie and Alan Yuille. *ICCV'17*

- Hierarchical Representations for Efficient Architecture Search [[pdf]](https://arxiv.org/abs/1711.00436)

  - Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu. *ICLR'18*

- Regularized Evolution for Image Classifier Architecture Search [[pdf]](https://arxiv.org/abs/1802.01548)

  - Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V Le. *Arxiv 1802*

- Weight Agnostic Neural Networks [[pdf]](https://arxiv.org/abs/1906.04358)

  - Adam Gaier, David Ha. *NeurIPS'19*

### Others

- Neural Architecture Optimization [[pdf]](https://arxiv.org/abs/1808.07233) [[code]](https://github.com/renqianluo/NAO)

  - Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu. *Arxiv 1808*

- DeepArchitect: Automatically Designing and Training Deep Architectures [[pdf]](https://arxiv.org/abs/1704.08792) [[code]](https://github.com/negrinho/deep_architect)

  - Renato Negrinho and Geoff Gordon. *Arxiv 1704*

- SMASH: One-Shot Model Architecture Search through HyperNetworks [[pdf]](https://arxiv.org/abs/1708.05344) [[code]](https://github.com/ajbrock/SMASH)

  - Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston. *ICLR'18*

- Simple and efficient architecture search for Convolutional Neural Networks [[pdf]](https://arxiv.org/abs/1711.04528)

  - Thomas Elsken, Jan-Hendrik Metzen, Frank Hutter. *ICLR'18 Workshop*

- Progressive Neural Architecture Search [[pdf]](https://arxiv.org/abs/1712.00559)

  - Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy. *Arxiv 1712*

- DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures [[pdf]](https://arxiv.org/abs/1806.08198)

  - [Jin-Dong Dong](https://markdtw.github.io), An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun. *ECCV'18*

- Neural Architecture Search with Bayesian Optimisation and Optimal Transport [[pdf]](https://arxiv.org/abs/1802.07191)

  - Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric Xing. *Arxiv 1802*

- Effective Building Block Design for Deep Convolutional Neural Networks using Search [[pdf]](https://arxiv.org/abs/1801.08577)

  - Jayanta K Dutta, Jiayi Liu, Unmesh Kurup, Mohak Shah. *Arxiv 1801*

- DARTS: Differentiable Architecture Search [[pdf]](https://arxiv.org/abs/1806.09055) [[code]](https://github.com/quark0/darts)

  - Hanxiao Liu, Karen Simonyan, Yiming Yang. *Arxiv 1806*

- Efficient Neural Architecture Search with Network Morphism [[pdf]](https://arxiv.org/abs/1806.10282) [[code]](https://github.com/jhfjhfj1/autokeras)

  - Haifeng Jin, Qingquan Song, Xia Hu. *Arxiv 1806*

- Searching for Efficient Multi-Scale Architectures for Dense Image Prediction [[pdf]](https://arxiv.org/abs/1809.04184) 

  - Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens. *Arxiv 1809*

- 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)

  - Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han. *ECCV'18*

- MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks [[pdf]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Gordon_MorphNet_Fast__CVPR_2018_paper.pdf)

  - Ariel Gordon, Elad Eban, Bo Chen, Ofir Nachum, Tien-Ju Yang, Edward Choi. *CVPR'18*

- Weight Agnostic Neural Networks [[pdf]](https://arxiv.org/abs/1906.04358)

  - Adam Gaier, David Ha. *NeurIPS'19*

- Towards Modular and Programmable Architecture Search [[pdf]](https://arxiv.org/abs/1909.13404) [[code]](https://github.com/negrinho/deep_architect)

  - Renato Negrinho, Darshan Patil, Nghia Le, Daniel Ferreira, Matthew Gormley, Geoffrey Gordon. *NeurIPS'19*

## Hyper-Parameter Search

- 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)

  - Tobias Domhan, Jost Tobias Springenberg, Frank Hutter. *IJCAI'15*

- Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization [[pdf]](https://arxiv.org/abs/1603.06560)

  - Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar. *ICLR'17*

- Learning Curve Prediction with Bayesian Neural Networks [[pdf]](http://ml.informatik.uni-freiburg.de/papers/17-ICLR-LCNet.pdf)

  - Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter. *ICLR'17*

- Accelerating Neural Architecture Search using Performance Prediction [[pdf]](https://arxiv.org/abs/1705.10823)

  - Bowen Baker, Otkrist Gupta, Ramesh Raskar, Nikhil Naik. *ICLR'18 Workshop*

- Hyperparameter Optimization: A Spectral Approach [[pdf]](https://arxiv.org/abs/1706.00764) [[code]](https://github.com/callowbird/Harmonica)

  - Elad Hazan, Adam Klivans, Yang Yuan. *NIPS DLTP Workshop 2017*

- Population Based Training of Neural Networks [[pdf]](https://arxiv.org/abs/1711.09846)

  - 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*

## Contributing



  



Please help contribute this list by contacting [me](https://markdtw.github.io/) or add [pull request](https://github.com/markdtw/awesome-architecture-search/pulls)

Markdown format:

```markdown

- Paper Name [[pdf]](link) [[code]](link)

  - Author 1, Author 2, Author 3. *Conference'Year*

```

## License

[![PDM](https://licensebuttons.net/p/mark/1.0/88x31.png)](https://creativecommons.org/publicdomain/zero/1.0/)

To the extent possible under law, [Mark Dong](https://markdtw.github.io/) has waived all copyright and related or neighboring rights to this work.