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..
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search
Authors: Bhavna Gopal, Arjun Sridhar, Tunhou Zhang, Yiran Chen
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase our method on an array of search spaces spanning various sizes and datasets. We accentuate the effectiveness of our shrunk spaces when used in one-shot search by achieving the best Top-1 accuracy in two different search spaces. Our method achieves a SOTA Top-1 accuracy of 77.6% in Image Net under mobile constraints, bestin-class Kendal-Tau, architectural diversity, and search space size. |
| Researcher Affiliation | Academia | Bhavna Gopal1 , Arjun Sridhar1 , Tunhou Zhang1 and Yiran Chen1 1Duke University EMAIL |
| Pseudocode | No | The paper describes the algorithm steps and shows a pipeline diagram (Figure 3) but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We investigate locality and our algorithm s performance on NASBench101, Trans NASBench Macro, NASBench301, and Shuffle Net V2. For a more detailed description of these search spaces and respective datasets, please reference the Appendix C. ... Image Net under mobile constraints serves as a canonical benchmark for all NAS techniques. |
| Dataset Splits | No | The paper mentions using training and evaluation but does not provide specific details on the split percentages or methodology for validation datasets. |
| Hardware Specification | No | The paper mentions '4 GPU days' but does not specify the type or model of GPU, CPU, or any other specific hardware used for the experiments. |
| Software Dependencies | No | The paper mentions using 'XGBBoost as our accuracy predictor' but does not provide specific version numbers for XGBoost or any other software dependencies. |
| Experiment Setup | No | The paper mentions a 'hyperparameter optimization scheme' but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations in the main text. |