Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/YIWEI-CHEN/awesome-automated-machine-learning

A curated list of awesome automated machine learning resources
https://github.com/YIWEI-CHEN/awesome-automated-machine-learning

List: awesome-automated-machine-learning

automated-deep-learning automated-feature-engineering automated-machine-learning automated-model-selection automl neural-architecture-search

Last synced: 16 days ago
JSON representation

A curated list of awesome automated machine learning resources

Host: GitHub
URL: https://github.com/YIWEI-CHEN/awesome-automated-machine-learning
Owner: YIWEI-CHEN
License: mit
Created: 2019-03-25T19:09:51.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2019-10-18T04:08:10.000Z (about 5 years ago)
Last Synced: 2024-04-10T15:58:01.012Z (9 months ago)
Topics: automated-deep-learning, automated-feature-engineering, automated-machine-learning, automated-model-selection, automl, neural-architecture-search
Size: 29.3 KB
Stars: 7
Watchers: 3
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

ultimate-awesome - awesome-automated-machine-learning - A curated list of awesome automated machine learning resources. (Other Lists / Monkey C Lists)

README

        # Awesome Automated Machine Learning [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

A curated list of awesome automated machine learning 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)

[awesome-architecture-search](https://github.com/markdtw/awesome-architecture-search), and

[awesome-NAS](https://github.com/D-X-Y/awesome-NAS).

Please feel free to [pull requests](https://github.com/YIWEI-CHEN/awesome-automated-machine-learning/pulls) 

or [open an issue](https://github.com/YIWEI-CHEN/awesome-automated-machine-learning/issues) to add papers.

Markdown format:

```markdown

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

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

```

## Table of Contents

- [Automated Feature Engieerning](#automated-feature-engineering)

    - [Exploration and Reduction](#exploration-and-reduction)

    - [Reinforcement Learning](#reinforcement-learning)

- [Automated Model Selection and Learning](#automated-model-selection-and-learning)

    - [Bayesian Optimization](#bayesian-optimization)

    - [Evolutionary Algorithm](#evolutionary-algorithm)

    - [Gradient-based Optimization](#gradient-based-optimization)

- [Automated Deep Learning](#automated-deep-learning)

    - [Bayesian Optimization](#bayesian-optimization)

    - [Reinforcement Learning](#reinforcement-learning)

    - [Evolutionary Algorithm](#evolutionary-algorithm)

    - [Gradient-based Optimization](#gradient-based-optimization)

    - [Test Performance on CIFAR-10](#test-performance-on-cifar-10)

- [Survey](#survey)

- [Related Resources](#related-resources)

## Automated Feature Engineering

### Exploration and Reduction

- Deep Feature Synthesis: Towards Automating Data Science Endeavors

    [[pdf]](https://ieeexplore.ieee.org/document/7344858)

    [[code]](https://github.com/Featuretools/featuretools)

     - James Max Kanter, Kalyan Veeramachaneni. 

- ExploreKit: Automatic Feature Generation and Selection

    [[pdf]](https://ieeexplore.ieee.org/document/7837936)

    [[code]](https://github.com/giladkatz/ExploreKit)

    - Gilad Katz, Eui Chul Richard Shin, Dawn Song. ICDM 2016

   

### Reinforcement Learning

- Feature Engineering for Predictive Modeling using Reinforcement Learning

    [[pdf]](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16564/16719)

    - Udayan Khurana, Horst Samulowitz, Deepak Turaga, AAAI 2018

## Automated Model Selection and Learning

### Bayesian Optimization

- Efficient and Robust Automated Machine Learning

    [[pdf]](https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf)

    [[code]](https://github.com/automl/auto-sklearn)

    - Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter. NIPS 2015

- Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

    [[pdf]](http://www.cs.ubc.ca/labs/beta/Projects/autoweka/papers/autoweka.pdf)

    [[code]](https://github.com/automl/autoweka)

    - Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown. KDD 2013

### Evolutionary Algorithm

- Automating Biomedical Data Science Through Tree-Based Pipeline Optimization

    [[pdf]](https://link.springer.com/chapter/10.1007/978-3-319-31204-0_9)

    [[code]](https://github.com/EpistasisLab/tpot)

    - Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore. 

      EvoApplications 2016

### Gradient-based Optimization

- TODO

## Automated Deep Learning

### Bayesian Optimization

- Auto-Keras: 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

### Reinforcement Learning

- Neural Architecture Search with Reinforcement Learning (NAS)

    [[pdf]](https://arxiv.org/abs/1611.01578)

    [[unofficial code]](https://github.com/titu1994/neural-architecture-search)

    - Barret Zoph and Quoc V. Le. ICLR 2017

- Learning Transferable Architectures for Scalable Image Recognition (NASNet)

    [[pdf]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zoph_Learning_Transferable_Architectures_CVPR_2018_paper.pdf)

    [[nasnet]](https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet)

    - Barret Zoph, Vijay Vasudevan, Jonathan Shlens, Quoc V. Le. CVPR 2018

- Efficient Neural Architecture Search via Parameter Sharing (ENAS)

    [[pdf]](http://proceedings.mlr.press/v80/pham18a.html) 

    [[code]](https://github.com/melodyguan/enas)

    - Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean. ICML 2018

- Path-Level Network Transformation for Efficient Architecture Search (PathLevel-EAS) 

    [[pdf]](http://proceedings.mlr.press/v80/cai18a/cai18a.pdf)

    [[code]](https://github.com/han-cai/PathLevel-EAS)

    - Han Cai, Jiacheng Yang,  Weinan Zhang, Song Han,Yong Yu. ICML 2018

    

### Evolutionary Algorithm

- Large-Scale Evolution of Image Classifiers (LargeEvoNet)

    [[pdf]](http://proceedings.mlr.press/v70/real17a)

    [[code]](https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet)

    - Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, 

      Alex Kurakin. ICML 2017

- Regularized Evolution for Image Classifier Architecture Search (AmoebaNet)

    [[pdf]](https://arxiv.org/abs/1802.01548)

    [[code]](https://github.com/tensorflow/tpu/tree/master/models/official/amoeba_net)

    [[code]](https://colab.research.google.com/github/google-research/google-research/blob/master/evolution/regularized_evolution_algorithm/regularized_evolution.ipynb)

    - Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V Le. AAAI 2019

    

### Gradient-based Optimization

- DARTS: Differentiable Architecture Search

    [[pdf]](https://openreview.net/pdf?id=S1eYHoC5FX)

    [[code]](https://github.com/quark0/darts)

    - Hanxiao Liu, Karen Simonyan, Yiming Yang. ICLR 2019

- ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

    [[pdf]](https://openreview.net/pdf?id=HylVB3AqYm)

    [[code]](https://github.com/MIT-HAN-LAB/ProxylessNAS)

    - Han Cai, Ligeng Zhu, Song Han. ICLR 2019

## Survey

- Techniques for Automated Machine Learning [[pdf]](https://arxiv.org/abs/1907.08908)

    - Yi-Wei Chen, Qingquan Song, Xia Hu. arXiv 1907

- Neural Architecture Search: A Survey [[pdf]](https://arxiv.org/abs/1808.05377)

    - Thomas Elsken, Jan Hendrik Metzen, Frank Hutter. arXiv 1808

- Taking Human out of Learning Applications: A Survey on Automated Machine Learning 

    [[pdf]](https://arxiv.org/abs/1810.13306)

    - Yao Quanming, Wang Mengshuo, Jair Escalante Hugo, Guyon Isabelle, Hu Yi-Qi, Li Yu-Feng, Tu Wei-Wei, Yang Qiang, 

      Yu Yang. arXiv 1810

## Related Resources

- [hibayesian/awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers)

- [markdtw/awesome-architecture-search](https://github.com/markdtw/awesome-architecture-search)

- [AutoML.org/Literature on Neural Architecture Search](https://www.ml4aad.org/automl/literature-on-neural-architecture-search/)

- [D-X-Y/awesome-NAS](https://github.com/D-X-Y/awesome-NAS)

- [weiaicunzai/awesome-image-classification](https://github.com/weiaicunzai/awesome-image-classification)

- [ChanChiChoi/awesome-automl](https://github.com/ChanChiChoi/awesome-automl)

- [songzhaozhe/Model-Architecture-Search-Paper-List](https://github.com/songzhaozhe/Model-Architecture-Search-Paper-List)