Efficient Neural Architecture Search via Proximal Iterations
Authors: Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu6664-6671
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we perform experiments with searching CNN and RNN structures. Four datasets, i.e., CIFAR-10, Image Net, PTB, WT2 will be utilized in our experiments (see Appendix B.1). |
| Researcher Affiliation | Collaboration | Quanming Yao,1 Ju Xu,3 Wei-Wei Tu,1 Zhanxing Zhu2,3,4 14Paradigm Inc, 2School of Mathematical Sciences, Peking University 3Center for Data Science, Peking University, 4Beijing Institute of Big Data Research (BIBDR) {yaoquanming, tuweiwei}@4paradigm.com, {xuju, zhanxing.zhu}@pku.edu.cn |
| Pseudocode | Yes | Algorithm 2 NASP: Efficient Neural Architecture Search with Proximal Iterations. |
| Open Source Code | Yes | The implementation of NASP is available at: https://github. com/xujinfan/NASP-codes. |
| Open Datasets | Yes | Four datasets, i.e., CIFAR-10, Image Net, PTB, WT2 will be utilized in our experiments (see Appendix B.1). ... we search architectures on CIFAR-10 ((Krizhevsky 2009)). |
| Dataset Splits | Yes | min A Lval (w , A) , s.t. w = arg min w Ltrain (w, A) ... where Lval (resp. Ltrain) is the loss on validation (resp. training) set... |
| Hardware Specification | No | The paper mentions "hundreds of GPU" and "GPU days" but does not specify exact GPU models or other hardware components used for their experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | Following (Liu, Simonyan, and Yang 2019), the convolutional cell consists of N = 7 nodes, and the network is obtained by stacking cells for 8 times; in the search process, we train a small network stacked by 8 cells with 50 epochs (see Appendix B.2). |