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..
Accelerating Neural Architecture Search via Proxy Data
Authors: Byunggook Na, Jisoo Mok, Hyeokjun Choe, Sungroh Yoon
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To empirically demonstrate the effectiveness, we conduct thorough experiments across diverse datasets, search spaces, and NAS algorithms. |
| Researcher Affiliation | Academia | 1 Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea 2 AIIS, ASRI, INMC, and Interdisciplinary Program in AI, Seoul National University, Seoul, South Korea |
| Pseudocode | No | The paper describes its proposed selection methods using textual descriptions and mathematical formulas, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/nabk89/NAS-with-Proxy-data. |
| Open Datasets | Yes | NAS-Bench-1shot1 is used as the primary testing platform to observe the effect of the proxy data on the search performance of three NAS algorithms on CIFAR-10... We conduct thorough experiments across diverse datasets, search spaces, and NAS algorithms. (Datasets mentioned: CIFAR-10, ImageNet, CIFAR-100, SVHN, all standard benchmarks and implicitly publicly available and cited). |
| Dataset Splits | No | To construct proxy data of size k, k examples among 50K training examples of CIFAR-10 are selected using selection methods. The selected examples are segregated into two parts: one for updating weight parameters and the other for updating architecture parameters. However, the paper does not specify the exact proportion or number of samples in these two parts. |
| Hardware Specification | Yes | For example, executing DARTS [Liu et al., 2019] using our selection method requires only 40 GPU minutes on a single Ge Force RTX 2080ti GPU. Owing to the reduction in search cost, searching on Image Net can be completed in 7.5 GPU hours on a single Tesla V100 GPU when incorporating the proposed selection into DARTS. |
| Software Dependencies | No | The paper mentions using 'Pytorch model zoo' but does not specify version numbers for PyTorch or any other software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | For a fair comparison, the same hyperparameter settings as those offered in NAS-Bench-1shot1 are used for all the tested NAS algorithms. ... It required 3.8 GPU days, i.e., 11.5 hours with eight V100 GPUs, with a batch size of 1024... PC-DARTS with ... a single V100 GPU with a batch size of 256... the weight decay factors for DARTS and Robust DARTS (L2) during search are set to be 0.0003 and 0.0243, respectively. |