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..
Neural Architecture Search without Training
Authors: Joe Mellor, Jack Turner, Amos Storkey, Elliot J Crowley
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We incorporate this measure into a simple algorithm that allows us to search for powerful networks without any training in a matter of seconds on a single GPU, and verify its effectiveness on NAS-Bench-101, NASBench-201, NATS-Bench, and Network Design Spaces. |
| Researcher Affiliation | Academia | 1Usher Institute, University of Edinburgh 2School of Informatics, University of Edinburgh 3School of Engineering, University of Edinburgh. Correspondence to: Joseph Mellor <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 NASWOT; Algorithm 2 Assisted Regularised EA AREA |
| Open Source Code | Yes | Code for reproducing our experiments is available at https://github.com/ Bayes Watch/nas-without-training. |
| Open Datasets | Yes | In this work we utilise NAS-Bench-101 (Ying et al., 2019), NASBench-201 (Dong & Yang, 2020), and NATS-Bench (Dong et al., 2021) to evaluate the effectiveness of our approach. NAS-Bench-101 consists of 423,624 neural networks... on the CIFAR-10 dataset... NAS-Bench-201 consists of 15,625 networks trained multiple times on CIFAR-10, CIFAR-100, and Image Net-16-120 (Chrabaszcz et al., 2017). We also make use of the Network Design Spaces (NDS) dataset (Radosavovic et al., 2019). |
| Dataset Splits | Yes | NAS-Bench-101 consists of 423,624 neural networks that have been trained exhaustively, with three different initialisations, on the CIFAR-10 dataset for 108 epochs. NAS-Bench-201 consists of 15,625 networks trained multiple times on CIFAR-10, CIFAR-100, and Image Net-16-120. In this work we utilise NAS-Bench-101 (Ying et al., 2019), NASBench-201 (Dong & Yang, 2020), and NATS-Bench (Dong et al., 2021) to evaluate the effectiveness of our approach. We sample networks at random and plot our score s on the untrained networks against their validation accuracies when trained. |
| Hardware Specification | Yes | Search times are recorded for a single 1080Ti GPU. |
| Software Dependencies | No | The paper mentions using NAS-Bench APIs, but it does not specify version numbers for any software dependencies like Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | NASWOT is our training-free algorithm (across 500 runs). REA uses evolutionary search to select an architecture (50 runs), Random selects one architecture (500 runs). AREA (assisted-REA) uses our score (Equation 2) to select the starting population for REA (50 runs). Search times for REA and AREA were calculated using the NASBench-101 API. We report NASWOT for sample sizes of N=10, N=100, and N=1000. |