Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Neural Architecture Search via Proximal Iterations
Authors: Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu6664-6671
AAAI 2020 | Venue PDF | 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) EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 2 NASP: Ef๏ฌcient 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). |