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..
Rethink DARTS Search Space and Renovate a New Benchmark
Authors: Jiuling Zhang, Zhiming Ding
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We re-implement twelve baselines and evaluate them across twelve conditions by combining two underexpolored influential factors: transductive robustness and discretization policy, to reasonably construct a benchmark upon multi-condition evaluation. |
| Researcher Affiliation | Academia | 1University of Chinese Academy of Sciences, Beijing, China 2Institute of Software, Chinese Academy of Sciences, Beijing, China. |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present any structured, code-like blocks describing a procedure. |
| Open Source Code | Yes | https://github.com/chaoji90/LHD |
| Open Datasets | Yes | Our benchmark is evaluated on the most commonly used standard benchmark datasets (CIFAR-10, CIFAR-100, SVHN) |
| Dataset Splits | Yes | architecture parameters α and operation weights ω are alternately optimized on validation set and training set respectively through a bilevel optimization objective. |
| Hardware Specification | Yes | 5.2h 3.1h on RTX 3090, like-for-like comparison after aligning all other conditions). |
| Software Dependencies | Yes | Software version for search and evaluation of the benchmark: torch 1.9, cuda 11.1, cudnn 8.2, driver version 460.67. But we also test the search and evaluation codes and verify the empirical memory overhead on more recent version: torch 1.10, cuda 11.3, cudnn 8.3 and driver 495.44. |
| Experiment Setup | Yes | Table 8. Hyperparameter settings of baselines in search. |