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# DARTS: Differentiable Architecture Search
DARTS: Differentiable Architecture Search is accepted by ICLR 2019.
In this paper, Hanxiao proposed a differentiable neural architecture search method, named as DARTS.
Recently, DARTS becomes very popular due to its simplicity and performance.
**Run DARTS on the NAS-Bench-201 search space**:
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 1 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 1 -1
```
# Citation
```
@inproceedings{liu2019darts,
title = {{DARTS}: Differentiable architecture search},
author = {Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2019}
}
```

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@@ -181,7 +181,7 @@ If researchers can provide better results with different hyper-parameters, we ar
- [4] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 1 -1`
- [5] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/ENAS.sh cifar10 1 -1`
- [6] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/RANDOM-NAS.sh cifar10 1 -1`
- [7] `bash ./scripts-search/algos/R-EA.sh -1`
- [7] `bash ./scripts-search/algos/R-EA.sh cifar10 3 -1`
- [8] `bash ./scripts-search/algos/Random.sh -1`
- [9] `bash ./scripts-search/algos/REINFORCE.sh 0.5 -1`
- [10] `bash ./scripts-search/algos/BOHB.sh -1`