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zhengxiawu merged 2 commits from dev into master 2 years ago
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      docs/notes/nas/Stronger NAS with Weaker Predictors.pdf

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# <center>**自动机器学习 AutoML**</center>
# **自动机器学习 AutoML**

<!-- <p align="center">
<strong><a href="#0papers">Papers</a></strong> •
@@ -23,25 +23,26 @@
- [Contributing (欢迎参与贡献)](#contributing)
- [Copyright notice](#copyright-notice)

- - -
---

<h2 id = "0papers">0.Papers（论文）</h2>

**Latest papers**: (All papers are also put in [doc/awesome_papers.md](https://git.openi.org.cn/PCL_AutoML/AutoML/src/branch/master/docs/awesome_papers.md))

| Title | Venue | Code | Note |
| :----------------------------------------------------------: | :-----: | :------------------------------------------------: | ------------------------------------------------------------ |
| [ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies](https://arxiv.org/pdf/2110.10423.pdf) | ArXiv.2021.10 | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/ProxyBO_%20Accelerating%20Neural%20Architecture%20Search%20via%20Bayesian%20Optimization%20with%20Zero-cost%20Proxies.pdf) |
| [Approximate Neural Architecture Search via Operation Distribution Learning](https://arxiv.org/abs/2111.04670) | ArXiv.2021.08 | | [LeiZhang](https://git.openi.org.cn/isleizhang):[Note](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Approximate%20Neural%20Architecture%20Search%20via%20Operation%20Distribution%20Learning%20.pdf) |
| [Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper.pdf) | CVPR | [Github](https://github.com/eric8607242/SGNAS) | |
| [Speedy Performance Estimation for Neural Architecture Search](https://arxiv.org/pdf/2006.04492.pdf) | NeurIPS | [Github](https://github.com/rubinxin/TSE) | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Speedy%20Performance%20Estimation%20for%20Neural%20Architecture%20Search.pdf) |
| [Generic Neural Architecture Search via Regression](https://proceedings.neurips.cc/paper/2021/file/aba53da2f6340a8b89dc96d09d0d0430-Paper.pdf) | NeurIPS | [Github](https://github.com/leeyeehoo/GenNAS) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Generic%20Neural%20Architecture%20Search%20via%20Regression.pdf) |
| [Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization](https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Learning_Latent_Architectural_Distribution_in_Differentiable_Neural_Architecture_Search_via_ICCV_2021_paper.html) | ICCV | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Learning%20Latent%20Architectural%20Distribution%20in%20Differentiable%20Neural%20Architecture%20Search%20via%20Variational%20Information%20Maximization.pdf) |
| [Not All Operations Contribute Equally: Hierarchical Operation-adaptive Predictor for Neural Architecture Search](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Not_All_Operations_Contribute_Equally_Hierarchical_Operation-Adaptive_Predictor_for_Neural_ICCV_2021_paper.pdf) | ICCV | | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Not%20All%20Operations%20Contribute%20Equally_%20Hierarchical%20Operation-adaptive%20Predictor%20for%20Neural%20Architecture%20Search.pdf) |
| [Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift](https://openaccess.thecvf.com/content/ICCV2021/papers/Peng_Pi-NAS_Improving_Neural_Architecture_Search_by_Reducing_Supernet_Training_Consistency_ICCV_2021_paper.pdf) | ICCV | [GitHub](https://github.com/Ernie1/Pi-NAS) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Pi-NAS_%20Improving%20Neural%20Architecture%20Search%20by%20Reducing%20Supernet%20Training%20Consistency%20Shift.pdf) |
| [RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_RANK-NOSH_Efficient_Predictor-Based_Architecture_Search_via_Non-Uniform_Successive_Halving_ICCV_2021_paper.pdf) | ICCV | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/RANK-NOSH_%20Efficient%20Predictor-Based%20Architecture%20Search%20via%20Non-Uniform%20Successive%20Halving.pdf) |

- - -
| Title | Venue | Code | Note |
| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------: | :------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies](https://arxiv.org/pdf/2110.10423.pdf) | ArXiv.2021.10 | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/ProxyBO_%20Accelerating%20Neural%20Architecture%20Search%20via%20Bayesian%20Optimization%20with%20Zero-cost%20Proxies.pdf) |
| [Approximate Neural Architecture Search via Operation Distribution Learning](https://arxiv.org/abs/2111.04670) | ArXiv.2021.08 | | [LeiZhang](https://git.openi.org.cn/isleizhang):[Note](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Approximate%20Neural%20Architecture%20Search%20via%20Operation%20Distribution%20Learning%20.pdf) |
| [Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper.pdf) | CVPR | [Github](https://github.com/eric8607242/SGNAS) | |
| [Speedy Performance Estimation for Neural Architecture Search](https://arxiv.org/pdf/2006.04492.pdf) | NeurIPS | [Github](https://github.com/rubinxin/TSE) | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Speedy%20Performance%20Estimation%20for%20Neural%20Architecture%20Search.pdf) |
| [Generic Neural Architecture Search via Regression](https://proceedings.neurips.cc/paper/2021/file/aba53da2f6340a8b89dc96d09d0d0430-Paper.pdf) | NeurIPS | [Github](https://github.com/leeyeehoo/GenNAS) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Generic%20Neural%20Architecture%20Search%20via%20Regression.pdf) |
| [Stronger NAS with Weaker Predictors](https://arxiv.org/abs/2102.10490) | NeurIPS | [Github](https://github.com/VITA-Group/WeakNAS) | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/branch/master/docs/notes/Stronger%20NAS%20with%20Weaker%20Predictors.pdf) |
| [Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization](https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Learning_Latent_Architectural_Distribution_in_Differentiable_Neural_Architecture_Search_via_ICCV_2021_paper.html) | ICCV | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Learning%20Latent%20Architectural%20Distribution%20in%20Differentiable%20Neural%20Architecture%20Search%20via%20Variational%20Information%20Maximization.pdf) |
| [Not All Operations Contribute Equally: Hierarchical Operation-adaptive Predictor for Neural Architecture Search](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Not_All_Operations_Contribute_Equally_Hierarchical_Operation-Adaptive_Predictor_for_Neural_ICCV_2021_paper.pdf) | ICCV | | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Not%20All%20Operations%20Contribute%20Equally_%20Hierarchical%20Operation-adaptive%20Predictor%20for%20Neural%20Architecture%20Search.pdf) |
| [Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift](https://openaccess.thecvf.com/content/ICCV2021/papers/Peng_Pi-NAS_Improving_Neural_Architecture_Search_by_Reducing_Supernet_Training_Consistency_ICCV_2021_paper.pdf) | ICCV | [GitHub](https://github.com/Ernie1/Pi-NAS) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Pi-NAS_%20Improving%20Neural%20Architecture%20Search%20by%20Reducing%20Supernet%20Training%20Consistency%20Shift.pdf) |
| [RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_RANK-NOSH_Efficient_Predictor-Based_Architecture_Search_via_Non-Uniform_Successive_Halving_ICCV_2021_paper.pdf) | ICCV | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/RANK-NOSH_%20Efficient%20Predictor-Based%20Architecture%20Search%20via%20Non-Uniform%20Successive%20Halving.pdf) |

---

<h2 id = "1introduction-and-tutorials">1.Introduction and Tutorials</h2>

@@ -49,12 +50,12 @@ Want to quickly learn AutoML？想尽快入门自动机器学习？看下面的

- Books 书籍
- Blogs 博客
- Video tutorials 视频教程
- Video tutorials 视频教程
- Brief introduction and slides 简介与ppt资料
- - -

<h2 id = "2automl-areas-and-papers">2.AutoML Areas and Papers</h2>
---

<h2 id = "2automl-areas-and-papers">2.AutoML Areas and Papers</h2>

- [Survey](https://git.openi.org.cn/PCL_AutoML/AutoML/src/branch/master/docs/awesome_papers.md#survey)
- [Theory](https://git.openi.org.cn/PCL_AutoML/AutoML/src/branch/master/docs/awesome_papers.md#theory)
@@ -62,73 +63,76 @@ Want to quickly learn AutoML？想尽快入门自动机器学习？看下面的
- [Hyper Paramater Optimization](https://git.openi.org.cn/PCL_AutoML/AutoML/src/branch/master/docs/awesome_papers.md#hyper-parameter-optimization)
- [Automatic Network Compression](https://git.openi.org.cn/PCL_AutoML/AutoML/src/branch/master/docs/awesome_papers.md#automatic-network-compression)

- - -
---

<h2 id = "3theory-and-survey">3.Theory and Survey (理论与综述)</h2>

Here are some articles on AutoML theory and survey.

### **Survey (综述文章)：**
| Title | Venue | Year | Code |Note|
| :----------------------------------------------------------- | :-------------------: | :--: | :-------: |:--:|
| [Automated Machine Learning](https://link.springer.com/book/10.1007/978-3-030-05318-5) | Springer Book | 2019 | - |
| [Neural architecture search: A survey](http://www.jmlr.org/papers/volume20/18-598/18-598.pdf) | JMLR | 2019 | - |
| [AutonoML: Towards an Integrated Framework for Autonomous Machine Learning](https://arxiv.org/pdf/2012.12600.pdf) | arXiv | 2020 | - |
| [Taking human out of learning applications: A survey on automated machine learning](https://arxiv.org/pdf/1810.13306.pdf) | arXiv | 2018 | - |
| [AutoML: A Survey of the State-of-the-Art](https://arxiv.org/pdf/1908.00709.pdf) | arXiv | 2019 | - |
| [A Survey on Neural Architecture Search](https://arxiv.org/pdf/1905.01392.pdf) | arXiv | 2019 | - |
| [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://arxiv.org/pdf/2006.02903.pdf) | ACM Computing Surveys | 2021 | - |
| [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://arxiv.org/pdf/2007.15745.pdf) | Neurocomputing | 2020 | [github](https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms)|

| Title | Venue | Year | Code | Note |
| :---------------------------------------------------------------------------------------------------------------------- | :-------------------: | :--: | :---------------------------------------------------------------------------------------------: | :--: |
| [Automated Machine Learning](https://link.springer.com/book/10.1007/978-3-030-05318-5) | Springer Book | 2019 | - | |
| [Neural architecture search: A survey](http://www.jmlr.org/papers/volume20/18-598/18-598.pdf) | JMLR | 2019 | - | |
| [AutonoML: Towards an Integrated Framework for Autonomous Machine Learning](https://arxiv.org/pdf/2012.12600.pdf) | arXiv | 2020 | - | |
| [Taking human out of learning applications: A survey on automated machine learning](https://arxiv.org/pdf/1810.13306.pdf) | arXiv | 2018 | - | |
| [AutoML: A Survey of the State-of-the-Art](https://arxiv.org/pdf/1908.00709.pdf) | arXiv | 2019 | - | |
| [A Survey on Neural Architecture Search](https://arxiv.org/pdf/1905.01392.pdf) | arXiv | 2019 | - | |
| [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://arxiv.org/pdf/2006.02903.pdf) | ACM Computing Surveys | 2021 | - | |
| [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://arxiv.org/pdf/2007.15745.pdf) | Neurocomputing | 2020 | [github](https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms) | |

### **Theory（理论文章）:**

#### Black-box optimization

| Title|Venue|Code| Note|Year|
| :--- | :-----: | :------: | ----------- | ------- |
| [BORE: Bayesian Optimization by Density-Ratio Estimation](http://proceedings.mlr.press/v139/tiao21a/tiao21a.pdf) |ICML |[GitHub](https://github.com/ltiao/bore)|[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/BORE_BayesianOptimization%20by_Density-Ratio_Estimation.pdf) |2021|
| [Meta Learning Black-Box Population-Based Optimizers](https://arxiv.org/abs/2103.03526) |ArXiv |[GitHub](https://github.com/optimization-toolbox/meta-learning-population-based-optimizers) |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Meta%20Learning%20Black-Box%20Population-Based%20Optimizers.pdf)|2021|
| [Transfer Bayesian Optimization](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Transfer_Bayesian_Optimization.pdf) | Note/Blog |[GitHub](https://git.openi.org.cn/PCL_AutoML/bbobenchmark) |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Transfer_Bayesian_Optimization.pdf)|2021|
| [HEBO: Heteroscedastic Evolutionary Bayesian Optimisation](https://arxiv.org/pdf/2012.03826v1.pdf) |NeurIPS 2020 black-box competition |[GitHub](https://github.com/huawei-noah/noah-research/tree/master/BO/HEBO)|[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/HEBO_%20Heteroscedastic%20Evolutionary%20BayesianOptimisation.pdf) |2020|
| [Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search](http://arxiv.org/abs/2007.00708) |NeurIPS |[GitHub](https://github.com/facebookresearch/LaMCTS)|[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Learning%20Search%20Space%20Partition%20for%20Black-box%20Optimization%20using%20Monte%20Carlo%20Tree%20Search.pdf) |2020|
| [Scalable Global Optimization via Local Bayesian Optimization](http://papers.nips.cc/paper/8788-scalable-global-optimization-via-local-bayesian-optimization.pdf) |NeuriPS |[GitHub](https://github.com/uber-research/TuRBO) |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Scalable_Global_Optimization_via_Local_Bayesian_Optimization.pdf)|2019|
| [Practical Transfer Learning for Bayesian Optimization](https://arxiv.org/abs/1802.02219) |ArXiv |- |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Practical_Transfer_Learning_for_Bayesian_Optimization.pdf)|2018|
| [Two-stage transfer surrogate model for automatic hyperparameter optimization](http://arxiv.org/abs/1802.02219) |ECML |[GitHub](https://github.com/wistuba/TST) |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Two-stage_transfer_surrogate_model_for_automatic_hyperparameter_optimization.pdf)|2016|
_ _ _
| Title | Venue | Code | Note | Year |
| :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :---------------------------------: | :--------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---- |
| [BORE: Bayesian Optimization by Density-Ratio Estimation](http://proceedings.mlr.press/v139/tiao21a/tiao21a.pdf) | ICML | [GitHub](https://github.com/ltiao/bore) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/BORE_BayesianOptimization%20by_Density-Ratio_Estimation.pdf) | 2021 |
| [Meta Learning Black-Box Population-Based Optimizers](https://arxiv.org/abs/2103.03526) | ArXiv | [GitHub](https://github.com/optimization-toolbox/meta-learning-population-based-optimizers) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Meta%20Learning%20Black-Box%20Population-Based%20Optimizers.pdf) | 2021 |
| [Transfer Bayesian Optimization](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Transfer_Bayesian_Optimization.pdf) | Note/Blog | [GitHub](https://git.openi.org.cn/PCL_AutoML/bbobenchmark) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Transfer_Bayesian_Optimization.pdf) | 2021 |
| [HEBO: Heteroscedastic Evolutionary Bayesian Optimisation](https://arxiv.org/pdf/2012.03826v1.pdf) | NeurIPS 2020 black-box competition | [GitHub](https://github.com/huawei-noah/noah-research/tree/master/BO/HEBO) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/HEBO_%20Heteroscedastic%20Evolutionary%20BayesianOptimisation.pdf) | 2020 |
| [Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search](http://arxiv.org/abs/2007.00708) | NeurIPS | [GitHub](https://github.com/facebookresearch/LaMCTS) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Learning%20Search%20Space%20Partition%20for%20Black-box%20Optimization%20using%20Monte%20Carlo%20Tree%20Search.pdf) | 2020 |
| [Scalable Global Optimization via Local Bayesian Optimization](http://papers.nips.cc/paper/8788-scalable-global-optimization-via-local-bayesian-optimization.pdf) | NeuriPS | [GitHub](https://github.com/uber-research/TuRBO) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Scalable_Global_Optimization_via_Local_Bayesian_Optimization.pdf) | 2019 |
| [Practical Transfer Learning for Bayesian Optimization](https://arxiv.org/abs/1802.02219) | ArXiv | - | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Practical_Transfer_Learning_for_Bayesian_Optimization.pdf) | 2018 |
| [Two-stage transfer surrogate model for automatic hyperparameter optimization](http://arxiv.org/abs/1802.02219) | ECML | [GitHub](https://github.com/wistuba/TST) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Two-stage_transfer_surrogate_model_for_automatic_hyperparameter_optimization.pdf) | 2016 |

<h2 id = "4code">4.Code (代码)</h2>
---

<h2 id = "4code">4.Code (代码)</h2>

_ _ _
---

<h2 id = "5scholars">5.Scholars</h2>

_ _ _
---

<h2 id = "6Thesis">6.Thesis</h2>

- - -
---

## 7.Datasets and Benchmarks

- - -
---

## 8.AutoML Challenges

- - -
---

## Applications

- - -
---

## Other Resources
- - -

---

## Contributing

If you are interested in contributing, please refer to [HERE](https://git.openi.org.cn/wufan/AutoML/src/branch/master/contributing.md) for instructions in contribution.

- - -
---

### Copyright notice

> ***[Notes]This Github repo can be used by following the corresponding licenses. I want to emphasis that it may contain some PDFs or thesis, which were downloaded by me and can only be used for academic purposes. The copyrights of these materials are owned by corresponding publishers or organizations. All this are for better adademic research. If any of the authors or publishers have concerns, please contact me to delete or replace them.***
> ***[Notes]This Github repo can be used by following the corresponding licenses. I want to emphasis that it may contain some PDFs or thesis, which were downloaded by me and can only be used for academic purposes. The copyrights of these materials are owned by corresponding publishers or organizations. All this are for better adademic research. If any of the authors or publishers have concerns, please contact me to delete or replace them.***

+ 193
- 194
docs/awesome_papers.md View File

@@ -40,224 +40,223 @@ Let's read some awesome Automated Machine Learning (AutoML) papers.
---

## Neural Architecture Search
<!-- highly referenced from https://github.com/D-X-Y/Awesome-AutoDL -->

<!-- highly referenced from https://github.com/D-X-Y/Awesome-AutoDL -->

### 2021

| Title | Venue | Code | Note |
| :----------------------------------------------------------: | :-----: | :------------------------------------------------: | ------------------------------------------------------------ |
| [ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies](https://arxiv.org/pdf/2110.10423.pdf) | ArXiv.2021.10 | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/ProxyBO_%20Accelerating%20Neural%20Architecture%20Search%20via%20Bayesian%20Optimization%20with%20Zero-cost%20Proxies.pdf) |
| [Approximate Neural Architecture Search via Operation Distribution Learning](https://arxiv.org/abs/2111.04670) | ArXiv.2021.08 | | [LeiZhang](https://git.openi.org.cn/isleizhang):[Note](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Approximate%20Neural%20Architecture%20Search%20via%20Operation%20Distribution%20Learning%20.pdf) |
| [Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper.pdf) | CVPR | [Github](https://github.com/eric8607242/SGNAS) | |
| [Speedy Performance Estimation for Neural Architecture Search](https://arxiv.org/pdf/2006.04492.pdf) | NeurIPS | [Github](https://github.com/rubinxin/TSE) | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Speedy%20Performance%20Estimation%20for%20Neural%20Architecture%20Search.pdf) |
| [Generic Neural Architecture Search via Regression](https://proceedings.neurips.cc/paper/2021/file/aba53da2f6340a8b89dc96d09d0d0430-Paper.pdf) | NeurIPS | [Github](https://github.com/leeyeehoo/GenNAS) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Generic%20Neural%20Architecture%20Search%20via%20Regression.pdf) |
| [Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization](https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Learning_Latent_Architectural_Distribution_in_Differentiable_Neural_Architecture_Search_via_ICCV_2021_paper.html) | ICCV | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Learning%20Latent%20Architectural%20Distribution%20in%20Differentiable%20Neural%20Architecture%20Search%20via%20Variational%20Information%20Maximization.pdf) |
| [Not All Operations Contribute Equally: Hierarchical Operation-adaptive Predictor for Neural Architecture Search](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Not_All_Operations_Contribute_Equally_Hierarchical_Operation-Adaptive_Predictor_for_Neural_ICCV_2021_paper.pdf) | ICCV | | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Not%20All%20Operations%20Contribute%20Equally_%20Hierarchical%20Operation-adaptive%20Predictor%20for%20Neural%20Architecture%20Search.pdf) |
| [Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift](https://openaccess.thecvf.com/content/ICCV2021/papers/Peng_Pi-NAS_Improving_Neural_Architecture_Search_by_Reducing_Supernet_Training_Consistency_ICCV_2021_paper.pdf) | ICCV | [GitHub](https://github.com/Ernie1/Pi-NAS) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Pi-NAS_%20Improving%20Neural%20Architecture%20Search%20by%20Reducing%20Supernet%20Training%20Consistency%20Shift.pdf) |
| [RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_RANK-NOSH_Efficient_Predictor-Based_Architecture_Search_via_Non-Uniform_Successive_Halving_ICCV_2021_paper.pdf) | ICCV | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/RANK-NOSH_%20Efficient%20Predictor-Based%20Architecture%20Search%20via%20Non-Uniform%20Successive%20Halving.pdf) |
| [Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition](https://arxiv.org/abs/2102.01063) | ICCV | [Github](https://github.com/idstcv/ZenNAS) | |
| [AutoFormer: Searching Transformers for Visual Recognition](https://arxiv.org/pdf/2107.00651.pdf) | ICCV | [GitHub](https://github.com/microsoft/AutoML) | |
| [LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search](https://arxiv.org/abs/2104.14545) | CVPR | [GitHub](https://github.com/researchmm/LightTrack) | |
| [Prioritized Architecture Sampling with Monto-Carlo Tree Search](https://arxiv.org/pdf/2103.11922.pdf) | CVPR | [GitHub](https://github.com/xiusu/NAS-Bench-Macro) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Prioritized%20Architecture%20Sampling%20with%20Monto-Carlo%20Tree%20Search.pdf) |
| [One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking](https://arxiv.org/abs/2104.00597) | CVPR | [GitHub](https://github.com/researchmm/NEAS) | |
| [DARTS-: Robustly Stepping out of Performance Collapse Without Indicators](https://openreview.net/pdf?id=KLH36ELmwIB) | ICLR | | |
| [Zero-Cost Proxies for Lightweight NAS](https://openreview.net/pdf?id=0cmMMy8J5q) | ICLR | | |
| [Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective](https://openreview.net/forum?id=Cnon5ezMHtu) | ICLR | [GitHub](https://github.com/VITA-Group/TENAS) | |
| [DrNAS: Dirichlet Neural Architecture Search](https://openreview.net/forum?id=9FWas6YbmB3) | ICLR | [GitHub](https://github.com/xiangning-chen/DrNAS) | |
| [Rethinking Architecture Selection in Differentiable NAS](https://openreview.net/forum?id=PKubaeJkw3) | ICLR | | |
| [Evolving Reinforcement Learning Algorithms](https://openreview.net/forum?id=0XXpJ4OtjW) | ICLR | | | | |
| Title | Venue | Code | Note |
| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------: | :---------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies](https://arxiv.org/pdf/2110.10423.pdf) | ArXiv.2021.10 | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/ProxyBO_%20Accelerating%20Neural%20Architecture%20Search%20via%20Bayesian%20Optimization%20with%20Zero-cost%20Proxies.pdf) |
| [Approximate Neural Architecture Search via Operation Distribution Learning](https://arxiv.org/abs/2111.04670) | ArXiv.2021.08 | | [LeiZhang](https://git.openi.org.cn/isleizhang):[Note](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Approximate%20Neural%20Architecture%20Search%20via%20Operation%20Distribution%20Learning%20.pdf) |
| [Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper.pdf) | CVPR | [Github](https://github.com/eric8607242/SGNAS) | |
| [Speedy Performance Estimation for Neural Architecture Search](https://arxiv.org/pdf/2006.04492.pdf) | NeurIPS | [Github](https://github.com/rubinxin/TSE) | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Speedy%20Performance%20Estimation%20for%20Neural%20Architecture%20Search.pdf) |
| [Generic Neural Architecture Search via Regression](https://proceedings.neurips.cc/paper/2021/file/aba53da2f6340a8b89dc96d09d0d0430-Paper.pdf) | NeurIPS | [Github](https://github.com/leeyeehoo/GenNAS) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Generic%20Neural%20Architecture%20Search%20via%20Regression.pdf) |
| [Stronger NAS with Weaker Predictors](https://arxiv.org/abs/2102.10490) | NeurIPS | [Github](https://github.com/VITA-Group/WeakNAS) | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/branch/master/docs/notes/Stronger%20NAS%20with%20Weaker%20Predictors.pdf) |
| [Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization](https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Learning_Latent_Architectural_Distribution_in_Differentiable_Neural_Architecture_Search_via_ICCV_2021_paper.html) | ICCV | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/Learning%20Latent%20Architectural%20Distribution%20in%20Differentiable%20Neural%20Architecture%20Search%20via%20Variational%20Information%20Maximization.pdf) |
| [Not All Operations Contribute Equally: Hierarchical Operation-adaptive Predictor for Neural Architecture Search](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Not_All_Operations_Contribute_Equally_Hierarchical_Operation-Adaptive_Predictor_for_Neural_ICCV_2021_paper.pdf) | ICCV | | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Not%20All%20Operations%20Contribute%20Equally_%20Hierarchical%20Operation-adaptive%20Predictor%20for%20Neural%20Architecture%20Search.pdf) |
| [Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift](https://openaccess.thecvf.com/content/ICCV2021/papers/Peng_Pi-NAS_Improving_Neural_Architecture_Search_by_Reducing_Supernet_Training_Consistency_ICCV_2021_paper.pdf) | ICCV | [GitHub](https://github.com/Ernie1/Pi-NAS) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Pi-NAS_%20Improving%20Neural%20Architecture%20Search%20by%20Reducing%20Supernet%20Training%20Consistency%20Shift.pdf) |
| [RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_RANK-NOSH_Efficient_Predictor-Based_Architecture_Search_via_Non-Uniform_Successive_Halving_ICCV_2021_paper.pdf) | ICCV | | [LeiZhang](https://git.openi.org.cn/isleizhang):[NOTE](https://git.openi.org.cn/PCL_AutoML/bbobenchmark/src/commit/55de41b8d14c0fd936629b25785d487dfd8a5f73/docs/NAS_paper_reading/RANK-NOSH_%20Efficient%20Predictor-Based%20Architecture%20Search%20via%20Non-Uniform%20Successive%20Halving.pdf) |
| [Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition](https://arxiv.org/abs/2102.01063) | ICCV | [Github](https://github.com/idstcv/ZenNAS) | |
| [AutoFormer: Searching Transformers for Visual Recognition](https://arxiv.org/pdf/2107.00651.pdf) | ICCV | [GitHub](https://github.com/microsoft/AutoML) | |
| [LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search](https://arxiv.org/abs/2104.14545) | CVPR | [GitHub](https://github.com/researchmm/LightTrack) | |
| [Prioritized Architecture Sampling with Monto-Carlo Tree Search](https://arxiv.org/pdf/2103.11922.pdf) | CVPR | [GitHub](https://github.com/xiusu/NAS-Bench-Macro) | [XiangFei](https://git.openi.org.cn/xfey):[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/Prioritized%20Architecture%20Sampling%20with%20Monto-Carlo%20Tree%20Search.pdf) |
| [One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking](https://arxiv.org/abs/2104.00597) | CVPR | [GitHub](https://github.com/researchmm/NEAS) | |
| [DARTS-: Robustly Stepping out of Performance Collapse Without Indicators](https://openreview.net/pdf?id=KLH36ELmwIB) | ICLR | | |
| [Zero-Cost Proxies for Lightweight NAS](https://openreview.net/pdf?id=0cmMMy8J5q) | ICLR | | |
| [Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective](https://openreview.net/forum?id=Cnon5ezMHtu) | ICLR | [GitHub](https://github.com/VITA-Group/TENAS) | |
| [DrNAS: Dirichlet Neural Architecture Search](https://openreview.net/forum?id=9FWas6YbmB3) | ICLR | [GitHub](https://github.com/xiangning-chen/DrNAS) | |
| [Rethinking Architecture Selection in Differentiable NAS](https://openreview.net/forum?id=PKubaeJkw3) | ICLR | | |
| [Evolving Reinforcement Learning Algorithms](https://openreview.net/forum?id=0XXpJ4OtjW) | ICLR | | |

### 2020


| Title | Venue | Code | Note |
| :----------------------------------------------------------- | :-----: | :----------------------------------------------------------: | ---- |
| [Designing Network Design Spaces](https://arxiv.org/pdf/2003.13678.pdf) | CVPR | [GitHub](https://github.com/facebookresearch/pycls) | |
| [Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search](https://papers.nips.cc/paper/2020/file/d072677d210ac4c03ba046120f0802ec-Paper.pdf) | NeurIPS | [GitHub](https://github.com/microsoft/Cream) | |
| [PyGlove: Symbolic Programming for Automated Machine Learning](https://proceedings.neurips.cc/paper/2020/file/012a91467f210472fab4e11359bbfef6-Paper.pdf) | NeurIPS | - | |
| [Does Unsupervised Architecture Representation Learning Help Neural Architecture Search](https://arxiv.org/abs/2006.06936) | NeurIPS | [GitHub](https://github.com/MSU-MLSys-Lab/arch2vec) | |
| [RandAugment: Practical Automated Data Augmentation with a Reduced Search Space](https://arxiv.org/abs/1909.13719) | NeurIPS | [GitHub](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | |
| [Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians](https://arxiv.org/pdf/2010.13514.pdf) | NeurIPS | [GitHub](https://github.com/pomonam/Self-Tuning-Networks) | |
| [A Study on Encodings for Neural Architecture Search](https://arxiv.org/abs/2007.04965) | NeurIPS | [GitHub](https://github.com/naszilla/naszilla) | |
| [AutoBSS: An Efficient Algorithm for Block Stacking Style Search](https://proceedings.neurips.cc/paper/2020/file/747d3443e319a22747fbb873e8b2f9f2-Paper.pdf) | NeurIPS | | |
| [Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS](https://proceedings.neurips.cc/paper/2020/file/13d4635deccc230c944e4ff6e03404b5-Paper.pdf) | NeurIPS | [GitHub](https://github.com/haolibai/APS-channel-search) | |
| [Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding](https://proceedings.neurips.cc/paper/2020/file/722caafb4825ef5d8670710fa29087cf-Paper.pdf) | NeurIPS | | |
| [Revisiting Parameter Sharing for Automatic Neural Channel Number Search](https://proceedings.neurips.cc/paper/2020/file/42cd63cb189c30ed03e42ce2c069566c-Paper.pdf) | NeurIPS | | |
| [Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search](https://arxiv.org/pdf/2007.00708.pdf) | NeurIPS | [GitHub](https://github.com/facebookresearch/LaMCTS) | |
| [Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search](https://arxiv.org/abs/1805.07440) | AAAI | [GitHub](https://github.com/linnanwang/AlphaX-NASBench101) | |
| [Are Labels Necessary for Neural Architecture Search?](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490766.pdf) | ECCV | - | |
| [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610528.pdf) | ECCV | - | |
| [Neural Predictor for Neural Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740647.pdf) | ECCV | - | |
| [BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520681.pdf) | ECCV | - | |
| [BATS: Binary ArchitecTure Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680307.pdf) | ECCV | - | |
| [AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530443.pdf) | ECCV | - | |
| [Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540001.pdf) | ECCV | - | |
| [Angle-based Search Space Shrinking for Neural Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630426.pdf) | ECCV | - | |
| [Anti-Bandit Neural Architecture Search for Model Defense](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580069.pdf) | ECCV | - | |
| [TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600120.pdf) | ECCV | [GitHub](https://github.com/AberHu/TF-NAS) | |
| [Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600460.pdf) | ECCV | [GitHub](https://github.com/xiaomi-automl/FairDARTS) | |
| [Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520171.pdf) | ECCV | - | |
| [DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720579.pdf) | ECCV | - | |
| [Optimizing Millions of Hyperparameters by Implicit Differentiation](https://arxiv.org/abs/1911.02590) | AISTATS | - | |
| [Evolving Machine Learning Algorithms From Scratch](https://arxiv.org/pdf/2003.03384.pdf) | ICML | - | |
| [Stabilizing Differentiable Architecture Search via Perturbation-based Regularization](https://arxiv.org/abs/2002.05283) | ICML | [GitHub](https://github.com/xiangning-chen/SmoothDARTS) | |
| [NADS: Neural Architecture Distribution Search for Uncertainty Awareness](https://arxiv.org/pdf/2006.06646.pdf) | ICML | - | |
| [Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data](https://arxiv.org/abs/1912.07768) | ICML | - | |
| Neural Architecture Search in a Proxy Validation Loss Landscape | ICML | - | |
| [UNAS: Differentiable Architecture Search Meets Reinforcement Learning](https://openaccess.thecvf.com/content_CVPR_2020/papers/Vahdat_UNAS_Differentiable_Architecture_Search_Meets_Reinforcement_Learning_CVPR_2020_paper.pdf) | CVPR | [GitHub](https://github.com/NVlabs/unas) | |
| [MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation](https://arxiv.org/pdf/2003.12238.pdf) | CVPR | [GitHub](https://github.com/chaoyanghe/MiLeNAS) | |
| [A Semi-Supervised Assessor of Neural Architectures](https://openaccess.thecvf.com/content_CVPR_2020/papers/Tang_A_Semi-Supervised_Assessor_of_Neural_Architectures_CVPR_2020_paper.pdf) | CVPR | - | |
| [Binarizing MobileNet via Evolution-based Searching](https://arxiv.org/abs/2005.06305) | CVPR | - | |
| [Rethinking Performance Estimation in Neural Architecture Search](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Rethinking_Performance_Estimation_in_Neural_Architecture_Search_CVPR_2020_paper.pdf) | CVPR | [GitHub](https://github.com/zhengxiawu/rethinking_performance_estimation_in_NAS) | |
| [APQ: Joint Search for Network Architecture, Pruning and Quantization Policy](http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.pdf) | CVPR | [GitHub](https://github.com/mit-han-lab/apq) | |
| [SGAS: Sequential Greedy Architecture Search](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_SGAS_Sequential_Greedy_Architecture_Search_CVPR_2020_paper.pdf) | CVPR | [Github](https://github.com/lightaime/sgas) | |
| [Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS](http://openaccess.thecvf.com/content_CVPR_2020/papers/Bender_Can_Weight_Sharing_Outperform_Random_Architecture_Search_An_Investigation_With_CVPR_2020_paper.pdf) | CVPR | - | |
| [FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions](https://arxiv.org/abs/2004.05565) | CVPR | [Github](https://github.com/facebookresearch/mobile-vision) | |
| [AdversarialNAS: Adversarial Neural Architecture Search for GANs](https://arxiv.org/pdf/1912.02037.pdf) | CVPR | [Github](https://github.com/chengaopro/AdversarialNAS) | |
| [When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks](https://arxiv.org/abs/1911.10695) | CVPR | [Github](https://github.com/gmh14/RobNets) | |
| [Block-wisely Supervised Neural Architecture Search with Knowledge Distillation](https://www.xiaojun.ai/papers/CVPR2020_04676.pdf) | CVPR | [Github](https://github.com/changlin31/DNA) | |
| [Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization](https://www.xiaojun.ai/papers/cvpr-2020-zhang.pdf) | CVPR | [Github](https://github.com/MiaoZhang0525/NSAS_FOR_CVPR) | |
| [Densely Connected Search Space for More Flexible Neural Architecture Search](https://arxiv.org/abs/1906.09607) | CVPR | [Github](https://github.com/JaminFong/DenseNAS) | |
| [EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/abs/1911.09070) | CVPR | - | |
| [NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr) | ICLR | [Github](https://github.com/D-X-Y/AutoDL-Projects) | |
| [Understanding Architectures Learnt by Cell-based Neural Architecture Search](https://openreview.net/forum?id=BJxH22EKPS) | ICLR | [GitHub](https://github.com/shuyao95/Understanding-NAS) | |
| [Evaluating The Search Phase of Neural Architecture Search](https://openreview.net/forum?id=H1loF2NFwr) | ICLR | | |
| [AtomNAS: Fine-Grained End-to-End Neural Architecture Search](https://openreview.net/forum?id=BylQSxHFwr) | ICLR | [GitHub](https://github.com/meijieru/AtomNAS) | |
| [Fast Neural Network Adaptation via Parameter Remapping and Architecture Search](https://openreview.net/forum?id=rklTmyBKPH) | ICLR | [GitHub](https://github.com/JaminFong/FNA) | |
| [Once for All: Train One Network and Specialize it for Efficient Deployment](https://openreview.net/forum?id=HylxE1HKwS) | ICLR | [GitHub](https://github.com/mit-han-lab/once-for-all) | |
| Efficient Transformer for Mobile Applications | ICLR | - | |
| PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search | ICLR | [GitHub](https://github.com/yuhuixu1993/PC-DARTS) | |
| Adversarial AutoAugment | ICLR | - | |
| [NAS evaluation is frustratingly hard](https://arxiv.org/abs/1912.12522) | ICLR | [GitHub](https://github.com/antoyang/NAS-Benchmark) | |
| [FasterSeg: Searching for Faster Real-time Semantic Segmentation](https://openreview.net/pdf?id=BJgqQ6NYvB) | ICLR | [GitHub](https://github.com/TAMU-VITA/FasterSeg) | |
| [Computation Reallocation for Object Detection](https://openreview.net/forum?id=SkxLFaNKwB) | ICLR | - | |
| Towards Fast Adaptation of Neural Architectures with Meta Learning | ICLR | - | |
| AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures | ICLR | - | |
| How to Own the NAS in Your Spare Time | ICLR | - | |
| Title | Venue | Code | Note |
| :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-----: | :-------------------------------------------------------------------------------: | ---- |
| [Designing Network Design Spaces](https://arxiv.org/pdf/2003.13678.pdf) | CVPR | [GitHub](https://github.com/facebookresearch/pycls) | |
| [Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search](https://papers.nips.cc/paper/2020/file/d072677d210ac4c03ba046120f0802ec-Paper.pdf) | NeurIPS | [GitHub](https://github.com/microsoft/Cream) | |
| [PyGlove: Symbolic Programming for Automated Machine Learning](https://proceedings.neurips.cc/paper/2020/file/012a91467f210472fab4e11359bbfef6-Paper.pdf) | NeurIPS | - | |
| [Does Unsupervised Architecture Representation Learning Help Neural Architecture Search](https://arxiv.org/abs/2006.06936) | NeurIPS | [GitHub](https://github.com/MSU-MLSys-Lab/arch2vec) | |
| [RandAugment: Practical Automated Data Augmentation with a Reduced Search Space](https://arxiv.org/abs/1909.13719) | NeurIPS | [GitHub](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) | |
| [Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians](https://arxiv.org/pdf/2010.13514.pdf) | NeurIPS | [GitHub](https://github.com/pomonam/Self-Tuning-Networks) | |
| [A Study on Encodings for Neural Architecture Search](https://arxiv.org/abs/2007.04965) | NeurIPS | [GitHub](https://github.com/naszilla/naszilla) | |
| [AutoBSS: An Efficient Algorithm for Block Stacking Style Search](https://proceedings.neurips.cc/paper/2020/file/747d3443e319a22747fbb873e8b2f9f2-Paper.pdf) | NeurIPS | | |
| [Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS](https://proceedings.neurips.cc/paper/2020/file/13d4635deccc230c944e4ff6e03404b5-Paper.pdf) | NeurIPS | [GitHub](https://github.com/haolibai/APS-channel-search) | |
| [Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding](https://proceedings.neurips.cc/paper/2020/file/722caafb4825ef5d8670710fa29087cf-Paper.pdf) | NeurIPS | | |
| [Revisiting Parameter Sharing for Automatic Neural Channel Number Search](https://proceedings.neurips.cc/paper/2020/file/42cd63cb189c30ed03e42ce2c069566c-Paper.pdf) | NeurIPS | | |
| [Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search](https://arxiv.org/pdf/2007.00708.pdf) | NeurIPS | [GitHub](https://github.com/facebookresearch/LaMCTS) | |
| [Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search](https://arxiv.org/abs/1805.07440) | AAAI | [GitHub](https://github.com/linnanwang/AlphaX-NASBench101) | |
| [Are Labels Necessary for Neural Architecture Search?](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490766.pdf) | ECCV | - | |
| [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610528.pdf) | ECCV | - | |
| [Neural Predictor for Neural Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740647.pdf) | ECCV | - | |
| [BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520681.pdf) | ECCV | - | |
| [BATS: Binary ArchitecTure Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680307.pdf) | ECCV | - | |
| [AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530443.pdf) | ECCV | - | |
| [Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540001.pdf) | ECCV | - | |
| [Angle-based Search Space Shrinking for Neural Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630426.pdf) | ECCV | - | |
| [Anti-Bandit Neural Architecture Search for Model Defense](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580069.pdf) | ECCV | - | |
| [TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600120.pdf) | ECCV | [GitHub](https://github.com/AberHu/TF-NAS) | |
| [Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600460.pdf) | ECCV | [GitHub](https://github.com/xiaomi-automl/FairDARTS) | |
| [Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520171.pdf) | ECCV | - | |
| [DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720579.pdf) | ECCV | - | |
| [Optimizing Millions of Hyperparameters by Implicit Differentiation](https://arxiv.org/abs/1911.02590) | AISTATS | - | |
| [Evolving Machine Learning Algorithms From Scratch](https://arxiv.org/pdf/2003.03384.pdf) | ICML | - | |
| [Stabilizing Differentiable Architecture Search via Perturbation-based Regularization](https://arxiv.org/abs/2002.05283) | ICML | [GitHub](https://github.com/xiangning-chen/SmoothDARTS) | |
| [NADS: Neural Architecture Distribution Search for Uncertainty Awareness](https://arxiv.org/pdf/2006.06646.pdf) | ICML | - | |
| [Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data](https://arxiv.org/abs/1912.07768) | ICML | - | |
| Neural Architecture Search in a Proxy Validation Loss Landscape | ICML | - | |
| [UNAS: Differentiable Architecture Search Meets Reinforcement Learning](https://openaccess.thecvf.com/content_CVPR_2020/papers/Vahdat_UNAS_Differentiable_Architecture_Search_Meets_Reinforcement_Learning_CVPR_2020_paper.pdf) | CVPR | [GitHub](https://github.com/NVlabs/unas) | |
| [MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation](https://arxiv.org/pdf/2003.12238.pdf) | CVPR | [GitHub](https://github.com/chaoyanghe/MiLeNAS) | |
| [A Semi-Supervised Assessor of Neural Architectures](https://openaccess.thecvf.com/content_CVPR_2020/papers/Tang_A_Semi-Supervised_Assessor_of_Neural_Architectures_CVPR_2020_paper.pdf) | CVPR | - | |
| [Binarizing MobileNet via Evolution-based Searching](https://arxiv.org/abs/2005.06305) | CVPR | - | |
| [Rethinking Performance Estimation in Neural Architecture Search](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Rethinking_Performance_Estimation_in_Neural_Architecture_Search_CVPR_2020_paper.pdf) | CVPR | [GitHub](https://github.com/zhengxiawu/rethinking_performance_estimation_in_NAS) | |
| [APQ: Joint Search for Network Architecture, Pruning and Quantization Policy](http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.pdf) | CVPR | [GitHub](https://github.com/mit-han-lab/apq) | |
| [SGAS: Sequential Greedy Architecture Search](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_SGAS_Sequential_Greedy_Architecture_Search_CVPR_2020_paper.pdf) | CVPR | [Github](https://github.com/lightaime/sgas) | |
| [Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS](http://openaccess.thecvf.com/content_CVPR_2020/papers/Bender_Can_Weight_Sharing_Outperform_Random_Architecture_Search_An_Investigation_With_CVPR_2020_paper.pdf) | CVPR | - | |
| [FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions](https://arxiv.org/abs/2004.05565) | CVPR | [Github](https://github.com/facebookresearch/mobile-vision) | |
| [AdversarialNAS: Adversarial Neural Architecture Search for GANs](https://arxiv.org/pdf/1912.02037.pdf) | CVPR | [Github](https://github.com/chengaopro/AdversarialNAS) | |
| [When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks](https://arxiv.org/abs/1911.10695) | CVPR | [Github](https://github.com/gmh14/RobNets) | |
| [Block-wisely Supervised Neural Architecture Search with Knowledge Distillation](https://www.xiaojun.ai/papers/CVPR2020_04676.pdf) | CVPR | [Github](https://github.com/changlin31/DNA) | |
| [Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization](https://www.xiaojun.ai/papers/cvpr-2020-zhang.pdf) | CVPR | [Github](https://github.com/MiaoZhang0525/NSAS_FOR_CVPR) | |
| [Densely Connected Search Space for More Flexible Neural Architecture Search](https://arxiv.org/abs/1906.09607) | CVPR | [Github](https://github.com/JaminFong/DenseNAS) | |
| [EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/abs/1911.09070) | CVPR | - | |
| [NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr) | ICLR | [Github](https://github.com/D-X-Y/AutoDL-Projects) | |
| [Understanding Architectures Learnt by Cell-based Neural Architecture Search](https://openreview.net/forum?id=BJxH22EKPS) | ICLR | [GitHub](https://github.com/shuyao95/Understanding-NAS) | |
| [Evaluating The Search Phase of Neural Architecture Search](https://openreview.net/forum?id=H1loF2NFwr) | ICLR | | |
| [AtomNAS: Fine-Grained End-to-End Neural Architecture Search](https://openreview.net/forum?id=BylQSxHFwr) | ICLR | [GitHub](https://github.com/meijieru/AtomNAS) | |
| [Fast Neural Network Adaptation via Parameter Remapping and Architecture Search](https://openreview.net/forum?id=rklTmyBKPH) | ICLR | [GitHub](https://github.com/JaminFong/FNA) | |
| [Once for All: Train One Network and Specialize it for Efficient Deployment](https://openreview.net/forum?id=HylxE1HKwS) | ICLR | [GitHub](https://github.com/mit-han-lab/once-for-all) | |
| Efficient Transformer for Mobile Applications | ICLR | - | |
| PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search | ICLR | [GitHub](https://github.com/yuhuixu1993/PC-DARTS) | |
| Adversarial AutoAugment | ICLR | - | |
| [NAS evaluation is frustratingly hard](https://arxiv.org/abs/1912.12522) | ICLR | [GitHub](https://github.com/antoyang/NAS-Benchmark) | |
| [FasterSeg: Searching for Faster Real-time Semantic Segmentation](https://openreview.net/pdf?id=BJgqQ6NYvB) | ICLR | [GitHub](https://github.com/TAMU-VITA/FasterSeg) | |
| [Computation Reallocation for Object Detection](https://openreview.net/forum?id=SkxLFaNKwB) | ICLR | - | |
| Towards Fast Adaptation of Neural Architectures with Meta Learning | ICLR | - | |
| AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures | ICLR | - | |
| How to Own the NAS in Your Spare Time | ICLR | - | |

### 2019

| Title | Venue | Code | Note |
| :----------------------------------------------------------- | :-----: | :----------------------------------------------------------: | ---- |
| [Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions](https://arxiv.org/abs/1903.03088) | ICLR | - | |
| [DATA: Differentiable ArchiTecture Approximation](http://papers.nips.cc/paper/8374-data-differentiable-architecture-approximation) | NeurIPS | - | |
| Random Search and Reproducibility for Neural Architecture Search | UAI | [GitHub](https://github.com/D-X-Y/NAS-Projects/blob/master/scripts-search/algos/RANDOM-NAS.sh) | |
| [Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition](https://www.aclweb.org/anthology/D19-1367.pdf/) | EMNLP | - | |
| [Continual and Multi-Task Architecture Search](https://www.aclweb.org/anthology/P19-1185.pdf) | ACL | - | |
| Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation | ICCV | - | |
| Multinomial Distribution Learning for Effective Neural Architecture Search | ICCV | - | |
| Searching for MobileNetV3 | ICCV | - | |
| [Multinomial Distribution Learning for Effective Neural Architecture Search](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zheng_Multinomial_Distribution_Learning_for_Effective_Neural_Architecture_Search_ICCV_2019_paper.pdf) | ICCV | [GitHub](https://github.com/tanglang96/MDENAS) | |
| [Fast and Practical Neural Architecture Search](http://openaccess.thecvf.com/content_ICCV_2019/papers/Cui_Fast_and_Practical_Neural_Architecture_Search_ICCV_2019_paper.pdf) | ICCV | | |
| [Teacher Guided Architecture Search](http://openaccess.thecvf.com/content_ICCV_2019/papers/Bashivan_Teacher_Guided_Architecture_Search_ICCV_2019_paper.pdf) | ICCV | - | |
| [AutoDispNet: Improving Disparity Estimation With AutoML](http://openaccess.thecvf.com/content_ICCV_2019/papers/Saikia_AutoDispNet_Improving_Disparity_Estimation_With_AutoML_ICCV_2019_paper.pdf) | ICCV | - | |
| [Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xiong_Resource_Constrained_Neural_Network_Architecture_Search_Will_a_Submodularity_Assumption_ICCV_2019_paper.pdf) | ICCV | - | |
| [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733) | ICCV | [Github](https://github.com/D-X-Y/NAS-Projects) | |
| [Evolving Space-Time Neural Architectures for Videos](https://arxiv.org/abs/1811.10636) | ICCV | [GitHub](https://sites.google.com/view/evanet-video) | |
| [AutoGAN: Neural Architecture Search for Generative Adversarial Networks](https://arxiv.org/pdf/1908.03835.pdf) | ICCV | [github](https://github.com/TAMU-VITA/AutoGAN) | |
| [Discovering Neural Wirings](https://arxiv.org/pdf/1906.00586.pdf) | NeurIPS | [Github](https://github.com/allenai/dnw) | |
| [Towards modular and programmable architecture search](https://arxiv.org/abs/1909.13404) | NeurIPS | [Github](https://github.com/negrinho/deep_architect) | |
| [Network Pruning via Transformable Architecture Search](https://arxiv.org/abs/1905.09717) | NeurIPS | [Github](https://github.com/D-X-Y/NAS-Projects) | |
| [Deep Active Learning with a NeuralArchitecture Search](https://arxiv.org/pdf/1811.07579.pdf) | NeurIPS | - | |
| DetNAS: Backbone Search for ObjectDetection | NeurIPS | - | |
| SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers | NeurIPS | - | |
| [Efficient Forward Architecture Search ](https://arxiv.org/abs/1905.13360) | NeurIPS | [Github](https://github.com/microsoft/petridishnn) | |
| Efficient Neural ArchitectureTransformation Search in Channel-Level for Object Detection | NeurIPS | - | |
| XNAS: Neural Architecture Search with Expert Advice | NeurIPS | - | |
| [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055) | ICLR | [github](https://github.com/quark0/darts) | |
| [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://openreview.net/pdf?id=HylVB3AqYm) | ICLR | [github](https://github.com/MIT-HAN-LAB/ProxylessNAS) | |
| [Graph HyperNetworks for Neural Architecture Search](https://arxiv.org/pdf/1810.05749.pdf) | ICLR | - | |
| [Learnable Embedding Space for Efficient Neural Architecture Compression](https://openreview.net/forum?id=S1xLN3C9YX) | ICLR | [github](https://github.com/Friedrich1006/ESNAC) | |
| [Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution](https://arxiv.org/abs/1804.09081) | ICLR | - | |
| [SNAS: stochastic neural architecture search](https://openreview.net/pdf?id=rylqooRqK7) | ICLR | - | |
| [NetTailor: Tuning the Architecture, Not Just the Weights](https://arxiv.org/abs/1907.00274) | CVPR | [Github](https://github.com/pedro-morgado/nettailor) | |
| [Searching for A Robust Neural Architecture in Four GPU Hours](http://xuanyidong.com/publication/gradient-based-diff-sampler/) | CVPR | [Github](https://github.com/D-X-Y/NAS-Projects) | |
| [ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation](http://openaccess.thecvf.com/content_CVPR_2019/papers/Dai_ChamNet_Towards_Efficient_Network_Design_Through_Platform-Aware_Model_Adaptation_CVPR_2019_paper.pdf) | CVPR | - | |
| [Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search](https://arxiv.org/pdf/1903.03777.pdf) | CVPR | [github](https://github.com/lixincn2015/Partial-Order-Pruning) | |
| [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://arxiv.org/abs/1812.03443) | CVPR | - | |
| [RENAS: Reinforced Evolutionary Neural Architecture Search ](https://arxiv.org/abs/1808.00193) | CVPR | - | |
| [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://arxiv.org/pdf/1901.02985.pdf) | CVPR | [GitHub](https://github.com/tensorflow/models/tree/master/research/deeplab) | |
| [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626) | CVPR | [Github](https://github.com/AnjieZheng/MnasNet-PyTorch) | |
| [MFAS: Multimodal Fusion Architecture Search](https://arxiv.org/pdf/1903.06496.pdf) | CVPR | - | |
| [A Neurobiological Evaluation Metric for Neural Network Model Search](https://arxiv.org/pdf/1805.10726.pdf) | CVPR | - | |
| [Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells](https://arxiv.org/abs/1810.10804) | CVPR | - | |
| Customizable Architecture Search for Semantic Segmentation | CVPR | - | |
| [Regularized Evolution for Image Classifier Architecture Search](https://arxiv.org/pdf/1802.01548.pdf) | AAAI | - | |
| AutoAugment: Learning Augmentation Policies from Data | CVPR | - | |
| Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules | ICML | - | |
| [The Evolved Transformer](https://arxiv.org/pdf/1901.11117.pdf) | ICML | [Github](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/evolved_transformer.py) | |
| EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks | ICML | - | |
| [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://arxiv.org/abs/1902.09635) | ICML | [Github](https://github.com/google-research/nasbench) | |
| [On Network Design Spaces for Visual Recognition](https://arxiv.org/abs/1905.13214) | ICCV | [Github](https://github.com/facebookresearch/nds) | |
| Title | Venue | Code | Note |
| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-----: | :--------------------------------------------------------------------------------------------------------: | ---- |
| [Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions](https://arxiv.org/abs/1903.03088) | ICLR | - | |
| [DATA: Differentiable ArchiTecture Approximation](http://papers.nips.cc/paper/8374-data-differentiable-architecture-approximation) | NeurIPS | - | |
| Random Search and Reproducibility for Neural Architecture Search | UAI | [GitHub](https://github.com/D-X-Y/NAS-Projects/blob/master/scripts-search/algos/RANDOM-NAS.sh) | |
| [Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition](https://www.aclweb.org/anthology/D19-1367.pdf/) | EMNLP | - | |
| [Continual and Multi-Task Architecture Search](https://www.aclweb.org/anthology/P19-1185.pdf) | ACL | - | |
| Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation | ICCV | - | |
| Multinomial Distribution Learning for Effective Neural Architecture Search | ICCV | - | |
| Searching for MobileNetV3 | ICCV | - | |
| [Multinomial Distribution Learning for Effective Neural Architecture Search](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zheng_Multinomial_Distribution_Learning_for_Effective_Neural_Architecture_Search_ICCV_2019_paper.pdf) | ICCV | [GitHub](https://github.com/tanglang96/MDENAS) | |
| [Fast and Practical Neural Architecture Search](http://openaccess.thecvf.com/content_ICCV_2019/papers/Cui_Fast_and_Practical_Neural_Architecture_Search_ICCV_2019_paper.pdf) | ICCV | | |
| [Teacher Guided Architecture Search](http://openaccess.thecvf.com/content_ICCV_2019/papers/Bashivan_Teacher_Guided_Architecture_Search_ICCV_2019_paper.pdf) | ICCV | - | |
| [AutoDispNet: Improving Disparity Estimation With AutoML](http://openaccess.thecvf.com/content_ICCV_2019/papers/Saikia_AutoDispNet_Improving_Disparity_Estimation_With_AutoML_ICCV_2019_paper.pdf) | ICCV | - | |
| [Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xiong_Resource_Constrained_Neural_Network_Architecture_Search_Will_a_Submodularity_Assumption_ICCV_2019_paper.pdf) | ICCV | - | |
| [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733) | ICCV | [Github](https://github.com/D-X-Y/NAS-Projects) | |
| [Evolving Space-Time Neural Architectures for Videos](https://arxiv.org/abs/1811.10636) | ICCV | [GitHub](https://sites.google.com/view/evanet-video) | |
| [AutoGAN: Neural Architecture Search for Generative Adversarial Networks](https://arxiv.org/pdf/1908.03835.pdf) | ICCV | [github](https://github.com/TAMU-VITA/AutoGAN) | |
| [Discovering Neural Wirings](https://arxiv.org/pdf/1906.00586.pdf) | NeurIPS | [Github](https://github.com/allenai/dnw) | |
| [Towards modular and programmable architecture search](https://arxiv.org/abs/1909.13404) | NeurIPS | [Github](https://github.com/negrinho/deep_architect) | |
| [Network Pruning via Transformable Architecture Search](https://arxiv.org/abs/1905.09717) | NeurIPS | [Github](https://github.com/D-X-Y/NAS-Projects) | |
| [Deep Active Learning with a NeuralArchitecture Search](https://arxiv.org/pdf/1811.07579.pdf) | NeurIPS | - | |
| DetNAS: Backbone Search for ObjectDetection | NeurIPS | - | |
| SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers | NeurIPS | - | |
| [Efficient Forward Architecture Search ](https://arxiv.org/abs/1905.13360) | NeurIPS | [Github](https://github.com/microsoft/petridishnn) | |
| Efficient Neural ArchitectureTransformation Search in Channel-Level for Object Detection | NeurIPS | - | |
| XNAS: Neural Architecture Search with Expert Advice | NeurIPS | - | |
| [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055) | ICLR | [github](https://github.com/quark0/darts) | |
| [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://openreview.net/pdf?id=HylVB3AqYm) | ICLR | [github](https://github.com/MIT-HAN-LAB/ProxylessNAS) | |
| [Graph HyperNetworks for Neural Architecture Search](https://arxiv.org/pdf/1810.05749.pdf) | ICLR | - | |
| [Learnable Embedding Space for Efficient Neural Architecture Compression](https://openreview.net/forum?id=S1xLN3C9YX) | ICLR | [github](https://github.com/Friedrich1006/ESNAC) | |
| [Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution](https://arxiv.org/abs/1804.09081) | ICLR | - | |
| [SNAS: stochastic neural architecture search](https://openreview.net/pdf?id=rylqooRqK7) | ICLR | - | |
| [NetTailor: Tuning the Architecture, Not Just the Weights](https://arxiv.org/abs/1907.00274) | CVPR | [Github](https://github.com/pedro-morgado/nettailor) | |
| [Searching for A Robust Neural Architecture in Four GPU Hours](http://xuanyidong.com/publication/gradient-based-diff-sampler/) | CVPR | [Github](https://github.com/D-X-Y/NAS-Projects) | |
| [ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation](http://openaccess.thecvf.com/content_CVPR_2019/papers/Dai_ChamNet_Towards_Efficient_Network_Design_Through_Platform-Aware_Model_Adaptation_CVPR_2019_paper.pdf) | CVPR | - | |
| [Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search](https://arxiv.org/pdf/1903.03777.pdf) | CVPR | [github](https://github.com/lixincn2015/Partial-Order-Pruning) | |
| [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://arxiv.org/abs/1812.03443) | CVPR | - | |
| [RENAS: Reinforced Evolutionary Neural Architecture Search ](https://arxiv.org/abs/1808.00193) | CVPR | - | |
| [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://arxiv.org/pdf/1901.02985.pdf) | CVPR | [GitHub](https://github.com/tensorflow/models/tree/master/research/deeplab) | |
| [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626) | CVPR | [Github](https://github.com/AnjieZheng/MnasNet-PyTorch) | |
| [MFAS: Multimodal Fusion Architecture Search](https://arxiv.org/pdf/1903.06496.pdf) | CVPR | - | |
| [A Neurobiological Evaluation Metric for Neural Network Model Search](https://arxiv.org/pdf/1805.10726.pdf) | CVPR | - | |
| [Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells](https://arxiv.org/abs/1810.10804) | CVPR | - | |
| Customizable Architecture Search for Semantic Segmentation | CVPR | - | |
| [Regularized Evolution for Image Classifier Architecture Search](https://arxiv.org/pdf/1802.01548.pdf) | AAAI | - | |
| AutoAugment: Learning Augmentation Policies from Data | CVPR | - | |
| Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules | ICML | - | |
| [The Evolved Transformer](https://arxiv.org/pdf/1901.11117.pdf) | ICML | [Github](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/evolved_transformer.py) | |
| EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks | ICML | - | |
| [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://arxiv.org/abs/1902.09635) | ICML | [Github](https://github.com/google-research/nasbench) | |
| [On Network Design Spaces for Visual Recognition](https://arxiv.org/abs/1905.13214) | ICCV | [Github](https://github.com/facebookresearch/nds) | |

### 2018

| Title | Venue | Code | Note |
| :----------------------------------------------------------- | :-----: | :----------------------------------------------------------: | ---- |
| Towards Automatically-Tuned Deep Neural Networks | BOOK | [GitHub](https://github.com/automl/Auto-PyTorch) | |
| [NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications](https://arxiv.org/pdf/1804.03230.pdf) | ECCV | [github](https://github.com/denru01/netadapt) | |
| [Efficient Architecture Search by Network Transformation](https://arxiv.org/pdf/1707.04873.pdf) | AAAI | [github](https://github.com/han-cai/EAS) | |
| [Learning Transferable Architectures for Scalable Image Recognition](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zoph_Learning_Transferable_Architectures_CVPR_2018_paper.pdf) | CVPR | [github](https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet) | |
| [N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning](https://openreview.net/forum?id=B1hcZZ-AW) | ICLR | - | |
| [A Flexible Approach to Automated RNN Architecture Generation](https://openreview.net/forum?id=SkOb1Fl0Z) | ICLR | - | |
| [Practical Block-wise Neural Network Architecture Generation](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhong_Practical_Block-Wise_Neural_CVPR_2018_paper.pdf) | CVPR | - | |
| [Path-Level Network Transformation for Efficient Architecture Search](https://arxiv.org/abs/1806.02639) | ICML | [github](https://github.com/han-cai/PathLevel-EAS) | |
| [Hierarchical Representations for Efficient Architecture Search](https://openreview.net/forum?id=BJQRKzbA-) | ICLR | - | |
| [Understanding and Simplifying One-Shot Architecture Search](http://proceedings.mlr.press/v80/bender18a/bender18a.pdf) | ICML | - | |
| [SMASH: One-Shot Model Architecture Search through HyperNetworks](https://arxiv.org/pdf/1708.05344.pdf) | ICLR | [github](https://github.com/ajbrock/SMASH) | |
| [Neural Architecture Optimization](https://arxiv.org/pdf/1808.07233.pdf) | NeurIPS | [github](https://github.com/renqianluo/NAO) | |
| [Searching for efficient multi-scale architectures for dense image prediction](https://papers.nips.cc/paper/8087-searching-for-efficient-multi-scale-architectures-for-dense-image-prediction.pdf) | NeurIPS | - | |
| [Progressive Neural Architecture Search](http://openaccess.thecvf.com/content_ECCV_2018/papers/Chenxi_Liu_Progressive_Neural_Architecture_ECCV_2018_paper.pdf) | ECCV | [github](https://github.com/chenxi116/PNASNet) | |
| [Neural Architecture Search with Bayesian Optimisation and Optimal Transport](https://arxiv.org/pdf/1802.07191.pdf) | NeurIPS | [github](https://github.com/kirthevasank/nasbot) | |
| [Differentiable Neural Network Architecture Search](https://openreview.net/pdf?id=BJ-MRKkwG) | ICLR-W | - | |
| [Accelerating Neural Architecture Search using Performance Prediction](https://arxiv.org/abs/1705.10823) | ICLR-W | - | |


| Title | Venue | Code | Note |
| :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-----: | :-------------------------------------------------------------------------------: | ---- |
| Towards Automatically-Tuned Deep Neural Networks | BOOK | [GitHub](https://github.com/automl/Auto-PyTorch) | |
| [NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications](https://arxiv.org/pdf/1804.03230.pdf) | ECCV | [github](https://github.com/denru01/netadapt) | |
| [Efficient Architecture Search by Network Transformation](https://arxiv.org/pdf/1707.04873.pdf) | AAAI | [github](https://github.com/han-cai/EAS) | |
| [Learning Transferable Architectures for Scalable Image Recognition](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zoph_Learning_Transferable_Architectures_CVPR_2018_paper.pdf) | CVPR | [github](https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet) | |
| [N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning](https://openreview.net/forum?id=B1hcZZ-AW) | ICLR | - | |
| [A Flexible Approach to Automated RNN Architecture Generation](https://openreview.net/forum?id=SkOb1Fl0Z) | ICLR | - | |
| [Practical Block-wise Neural Network Architecture Generation](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhong_Practical_Block-Wise_Neural_CVPR_2018_paper.pdf) | CVPR | - | |
| [Path-Level Network Transformation for Efficient Architecture Search](https://arxiv.org/abs/1806.02639) | ICML | [github](https://github.com/han-cai/PathLevel-EAS) | |
| [Hierarchical Representations for Efficient Architecture Search](https://openreview.net/forum?id=BJQRKzbA-) | ICLR | - | |
| [Understanding and Simplifying One-Shot Architecture Search](http://proceedings.mlr.press/v80/bender18a/bender18a.pdf) | ICML | - | |
| [SMASH: One-Shot Model Architecture Search through HyperNetworks](https://arxiv.org/pdf/1708.05344.pdf) | ICLR | [github](https://github.com/ajbrock/SMASH) | |
| [Neural Architecture Optimization](https://arxiv.org/pdf/1808.07233.pdf) | NeurIPS | [github](https://github.com/renqianluo/NAO) | |
| [Searching for efficient multi-scale architectures for dense image prediction](https://papers.nips.cc/paper/8087-searching-for-efficient-multi-scale-architectures-for-dense-image-prediction.pdf) | NeurIPS | - | |
| [Progressive Neural Architecture Search](http://openaccess.thecvf.com/content_ECCV_2018/papers/Chenxi_Liu_Progressive_Neural_Architecture_ECCV_2018_paper.pdf) | ECCV | [github](https://github.com/chenxi116/PNASNet) | |
| [Neural Architecture Search with Bayesian Optimisation and Optimal Transport](https://arxiv.org/pdf/1802.07191.pdf) | NeurIPS | [github](https://github.com/kirthevasank/nasbot) | |
| [Differentiable Neural Network Architecture Search](https://openreview.net/pdf?id=BJ-MRKkwG) | ICLR-W | - | |
| [Accelerating Neural Architecture Search using Performance Prediction](https://arxiv.org/abs/1705.10823) | ICLR-W | - | |

### 2017

| Title | Venue | Code | Note |
| :----------------------------------------------------------- | :-------: | :-----------------------------------------------: | ---- |
| [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578) | ICLR | - | |
| [Designing Neural Network Architectures using Reinforcement Learning](https://openreview.net/pdf?id=S1c2cvqee) | ICLR | - | |
| [Neural Optimizer Search with Reinforcement Learning](http://proceedings.mlr.press/v70/bello17a/bello17a.pdf) | ICML | - | |
| [Learning Curve Prediction with Bayesian Neural Networks](http://ml.informatik.uni-freiburg.de/papers/17-ICLR-LCNet.pdf) | ICLR | - | |
| [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://arxiv.org/abs/1603.06560) | ICLR | - | |
| [Hyperparameter Optimization: A Spectral Approach](https://arxiv.org/abs/1706.00764) | NeurIPS-W | [github](https://github.com/callowbird/Harmonica) | |
| Learning to Compose Domain-Specific Transformations for Data Augmentation | NeurIPS | - | |
| Title | Venue | Code | Note |
| :-------------------------------------------------------------------------------------------------------------------- | :-------: | :--------------------------------------------: | ---- |
| [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578) | ICLR | - | |
| [Designing Neural Network Architectures using Reinforcement Learning](https://openreview.net/pdf?id=S1c2cvqee) | ICLR | - | |
| [Neural Optimizer Search with Reinforcement Learning](http://proceedings.mlr.press/v70/bello17a/bello17a.pdf) | ICML | - | |
| [Learning Curve Prediction with Bayesian Neural Networks](http://ml.informatik.uni-freiburg.de/papers/17-ICLR-LCNet.pdf) | ICLR | - | |
| [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://arxiv.org/abs/1603.06560) | ICLR | - | |
| [Hyperparameter Optimization: A Spectral Approach](https://arxiv.org/abs/1706.00764) | NeurIPS-W | [github](https://github.com/callowbird/Harmonica) | |
| Learning to Compose Domain-Specific Transformations for Data Augmentation | NeurIPS | - | |

### 2012-2016

| Title | Venue | Code | Note |
| :----------------------------------------------------------- | :---: | :----------------------------------------------------------: | ---- |
| Title | Venue | Code | Note |
| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---: | :-------------------------------------------------------: | ---- |
| [Speeding up Automatic Hyperparameter Optimization of Deep Neural Networksby Extrapolation of Learning Curves](http://ml.informatik.uni-freiburg.de/papers/15-IJCAI-Extrapolation_of_Learning_Curves.pdf) | IJCAI | [github](https://github.com/automl/pylearningcurvepredictor) | |




#### arXiv

| Title | Date | Code | Note |
| :----------------------------------------------------------- | :-----: | :----------------------------------------------------------: | ------------------------------------------------------------ |
| [AutoHAS: Differentiable Hyper-parameter and Architecture Search](https://arxiv.org/pdf/2006.03656.pdf) | 2020.06 | - | |
| [FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function](https://arxiv.org/abs/2006.02049) | 2020.06 | [github](https://github.com/facebookresearch/mobile-vision/blob/main/mobile_cv/arch/fbnet_v2/fbnet_modeldef_cls_fbnetv3.py) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/FBNetV3_%20Joint%20Architecture-Recipe%20Search%20using%20Predictor%20Pretraining.pdf)|
| [Population Based Training of Neural Networks](https://arxiv.org/abs/1711.09846) | 2017.11 | - | |
| [NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search](https://arxiv.org/pdf/1810.03522.pdf) | 2018.10 | - | |
| [Training Frankenstein’s Creature to Stack: HyperTree Architecture Search](https://arxiv.org/pdf/1810.11714.pdf) | 2018.10 | - | |
| [Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search](https://arxiv.org/pdf/1901.07261.pdf) | 2019.01 | [github](https://github.com/falsr/FALSR) | |
| Title | Date | Code | Note |
| :------------------------------------------------------------------------------------------------------------------- | :-----: | :----------------------------------------------------------------------------------------------------------------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [AutoHAS: Differentiable Hyper-parameter and Architecture Search](https://arxiv.org/pdf/2006.03656.pdf) | 2020.06 | - | |
| [FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function](https://arxiv.org/abs/2006.02049) | 2020.06 | [github](https://github.com/facebookresearch/mobile-vision/blob/main/mobile_cv/arch/fbnet_v2/fbnet_modeldef_cls_fbnetv3.py) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/62eed82bdd15b338ca09dfea26b5a8a77debd376/docs/notes/nas/FBNetV3_%20Joint%20Architecture-Recipe%20Search%20using%20Predictor%20Pretraining.pdf) |
| [Population Based Training of Neural Networks](https://arxiv.org/abs/1711.09846) | 2017.11 | - | |
| [NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search](https://arxiv.org/pdf/1810.03522.pdf) | 2018.10 | - | |
| [Training Frankenstein’s Creature to Stack: HyperTree Architecture Search](https://arxiv.org/pdf/1810.11714.pdf) | 2018.10 | - | |
| [Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search](https://arxiv.org/pdf/1901.07261.pdf) | 2019.01 | [github](https://github.com/falsr/FALSR) | |

---

## Hyper Parameter Optimization

| Title|Venue|Code| Note|Year|
| :--- | :-----: | :------: | ----------- | ------- |
| [BORE: Bayesian Optimization by Density-Ratio Estimation](http://proceedings.mlr.press/v139/tiao21a/tiao21a.pdf) |ICML |[GitHub](https://github.com/ltiao/bore)|[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/BORE_BayesianOptimization%20by_Density-Ratio_Estimation.pdf) |2021|
| [Meta Learning Black-Box Population-Based Optimizers](https://arxiv.org/abs/2103.03526) |ArXiv |[GitHub](https://github.com/optimization-toolbox/meta-learning-population-based-optimizers) |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Meta%20Learning%20Black-Box%20Population-Based%20Optimizers.pdf)|2021|
| [Transfer Bayesian Optimization](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Transfer_Bayesian_Optimization.pdf) | Note/Blog |[GitHub](https://git.openi.org.cn/PCL_AutoML/bbobenchmark) |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Transfer_Bayesian_Optimization.pdf)|2021|
| [HEBO: Heteroscedastic Evolutionary Bayesian Optimisation](https://arxiv.org/pdf/2012.03826v1.pdf) |NeurIPS 2020 black-box competition |[GitHub](https://github.com/huawei-noah/noah-research/tree/master/BO/HEBO)|[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/HEBO_%20Heteroscedastic%20Evolutionary%20BayesianOptimisation.pdf) |2020|
| [Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search](http://arxiv.org/abs/2007.00708) |NeurIPS |[GitHub](https://github.com/facebookresearch/LaMCTS)|[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Learning%20Search%20Space%20Partition%20for%20Black-box%20Optimization%20using%20Monte%20Carlo%20Tree%20Search.pdf) |2020|
| [Scalable Global Optimization via Local Bayesian Optimization](http://papers.nips.cc/paper/8788-scalable-global-optimization-via-local-bayesian-optimization.pdf) |NeuriPS |[GitHub](https://github.com/uber-research/TuRBO) |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Scalable_Global_Optimization_via_Local_Bayesian_Optimization.pdf)|2019|
| [Practical Transfer Learning for Bayesian Optimization](https://arxiv.org/abs/1802.02219) |ArXiv |- |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Practical_Transfer_Learning_for_Bayesian_Optimization.pdf)|2018|
| [Two-stage transfer surrogate model for automatic hyperparameter optimization](http://arxiv.org/abs/1802.02219) |ECML |[GitHub](https://github.com/wistuba/TST) |[NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Two-stage_transfer_surrogate_model_for_automatic_hyperparameter_optimization.pdf)|2016|
| Title | Venue | Code | Note | Year |
| :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :---------------------------------: | :--------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---- |
| [BORE: Bayesian Optimization by Density-Ratio Estimation](http://proceedings.mlr.press/v139/tiao21a/tiao21a.pdf) | ICML | [GitHub](https://github.com/ltiao/bore) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/BORE_BayesianOptimization%20by_Density-Ratio_Estimation.pdf) | 2021 |
| [Meta Learning Black-Box Population-Based Optimizers](https://arxiv.org/abs/2103.03526) | ArXiv | [GitHub](https://github.com/optimization-toolbox/meta-learning-population-based-optimizers) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Meta%20Learning%20Black-Box%20Population-Based%20Optimizers.pdf) | 2021 |
| [Transfer Bayesian Optimization](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Transfer_Bayesian_Optimization.pdf) | Note/Blog | [GitHub](https://git.openi.org.cn/PCL_AutoML/bbobenchmark) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Transfer_Bayesian_Optimization.pdf) | 2021 |
| [HEBO: Heteroscedastic Evolutionary Bayesian Optimisation](https://arxiv.org/pdf/2012.03826v1.pdf) | NeurIPS 2020 black-box competition | [GitHub](https://github.com/huawei-noah/noah-research/tree/master/BO/HEBO) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/HEBO_%20Heteroscedastic%20Evolutionary%20BayesianOptimisation.pdf) | 2020 |
| [Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search](http://arxiv.org/abs/2007.00708) | NeurIPS | [GitHub](https://github.com/facebookresearch/LaMCTS) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Learning%20Search%20Space%20Partition%20for%20Black-box%20Optimization%20using%20Monte%20Carlo%20Tree%20Search.pdf) | 2020 |
| [Scalable Global Optimization via Local Bayesian Optimization](http://papers.nips.cc/paper/8788-scalable-global-optimization-via-local-bayesian-optimization.pdf) | NeuriPS | [GitHub](https://github.com/uber-research/TuRBO) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Scalable_Global_Optimization_via_Local_Bayesian_Optimization.pdf) | 2019 |
| [Practical Transfer Learning for Bayesian Optimization](https://arxiv.org/abs/1802.02219) | ArXiv | - | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Practical_Transfer_Learning_for_Bayesian_Optimization.pdf) | 2018 |
| [Two-stage transfer surrogate model for automatic hyperparameter optimization](http://arxiv.org/abs/1802.02219) | ECML | [GitHub](https://github.com/wistuba/TST) | [NOTE](https://git.openi.org.cn/PCL_AutoML/AutoML/src/commit/ab479b264106197958d7cfdf6be34f10ab5445e6/docs/notes/hpo/Two-stage_transfer_surrogate_model_for_automatic_hyperparameter_optimization.pdf) | 2016 |

---
## Automatic Network Compression

## Automatic Network Compression

BIN
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