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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.
| Date | Stars |
|---|---|
| 2026-07-24 | 856 |
| 2026-07-25 | 856 |
| 2026-07-28 | 856 |
| 2026-07-30 | 856 |
| 2026-08-06 | 856 |
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# awesome-AutoML-and-Lightweight-Models A list of high-quality (newest) AutoML works and lightweight models including **1.) Neural Architecture Search**, **2.) Lightweight Structures**, **3.) Model Compression, Quantization and Acceleration**, **4.) Hyperparameter Optimization**, **5.) Automated Feature Engineering**. This repo is aimed to provide the info for AutoML research (especially for the lightweight models). Welcome to PR the works (papers, repositories) that are missed by the repo. ## 1.) Neural Architecture Search ### **[Papers]** **Gradient:** - [When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks](https://arxiv.org/abs/1911.10695) | [**CVPR 2020**] + [gmh14/RobNets](https://github.com/gmh14/RobNets) | [Pytorch] - [Searching for A Robust Neural Architecture in Four GPU Hours](https://xuanyidong.com/publication/cvpr-2019-gradient-based-diff-sampler/) | [**CVPR 2019**] + [D-X-Y/GDAS](https://github.com/D-X-Y/GDAS) | [Pytorch] - [ASAP: Architecture Search, Anneal and Prune](https://arxiv.org/abs/1904.04123) | [2019/04] - [Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours](https://arxiv.org/abs/1904.02877#) | [2019/04] + [dstamoulis/single-path-nas](https://github.com/dstamoulis/single-path-nas) | [Tensorflow] - [Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes](https://ieeexplore.ieee.org/document/8676019) | [**IEEE Access 2019**] - [sharpDARTS: Faster and More Accurate Differentiable Architecture Search](https://arxiv.org/abs/1903.09900) | [2019/03] - [Learning Implicitly Recurrent CNNs Through Parameter Sharing](https://arxiv.org/abs/1902.09701) | [**ICLR 2019**] + [lolemacs/soft-sharing](https://github.com/lolemacs/soft-sharing) | [Pytorch] - [Probabilistic Neural Architecture Search](https://arxiv.org/abs/1902.05116) | [2019/02] - [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://arxiv.org/abs/1901.02985) | [2019/01] - [SNAS: Stochastic Neural Architecture Search](https://arxiv.org/abs/1812.09926) | [**ICLR 2019**] - [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://arxiv.org/abs/1812.03443) | [2018/12] - [Neural Architecture Optimization](http://papers.nips.cc/paper/8007-neural-architecture-optimization) | [**NIPS 2018**] + [renqianluo/NAO](https://github.com/renqianluo/NAO) | [Tensorflow] - [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055) | [2018/06] + [quark0/darts](https://github.com/quark0/darts) | [Pytorch] + [khanrc/pt.darts](https://github.com/khanrc/pt.darts) | [Pytorch] + [dragen1860/DARTS-PyTorch](https://github.com/dragen1860/DARTS-PyTorch) | [Pytorch] **Reinforcement Learning:** - [Template-Based Automatic Search of Compact Semantic Segmentation Architectures](https://arxiv.org/abs/1904.02365) | [2019/04] - [Understanding Neural Architecture Search Techniques](https://arxiv.org/abs/1904.00438) | [2019/03] - [Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search](https://arxiv.org/abs/1901.07261) | [2019/01] + [falsr/FALSR](https://github.com/falsr/FALSR) | [Tensorflow] - [Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search](https://arxiv.org/abs/1901.01074) | [2019/01] + [moremnas/MoreMNAS](https://github.com/moremnas/MoreMNAS) | [Tensorflow] - [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://arxiv.org/abs/1812.00332) | [**ICLR 2019**] + [MIT-HAN-LAB/ProxylessNAS](https://github.com/MIT-HAN-LAB/ProxylessNAS) | [Pytorch, Tensorflow] - [Transfer Learning with Neural AutoML](http://papers.nips.cc/paper/8056-transfer-learning-with-neural-automl) | [**NIPS 2018**] - [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012) | [2018/07] + [wandering007/nasnet-pytorch](https://github.com/wandering
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Li Yang, PhD · Ontario Tech University · Canada
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Zoran Pandovski · @mindsdb
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0ef4135dd204a684, topic:quantization, topic:model-compression, desc:quantization
matched fp:0ef4135dd204a684, topic:pytorch, topic:tensorflow
matched fp:0ef4135dd204a684, topic:awesome-list, desc:a list of, readme:a list of