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..
MOTE-NAS: Multi-Objective Training-based Estimate for Efficient Neural Architecture Search
Authors: Yuming Zhang, Jun Hsieh, Xin Li, Ming-Ching Chang, Chun-Chieh Lee, Kuo-Chin Fan
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on NASBench-201 show MOTE-NAS achieves 94.32% accuracy on CIFAR-10, 72.81% on CIFAR-100, and 46.38% on Image Net-16-120, outperforming NTKbased NAS approaches. An evaluation-free (EF) version of MOTE-NAS delivers high efficiency in only 5 minutes, delivering a model more accurate than KNAS. |
| Researcher Affiliation | Academia | Yu-Ming Zhang1 Jun-Wei Hsieh2 Xin Li3 Ming-Ching Chang3 Chun-Chieh Lee1 Kuo-Chin Fan1 1National Central University 3University at Albany 2National Yang Ming Chiao Tung University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | We have described the experimental details in the paper and supplementary materials as much as possible, and we will make the code public on github. |
| Open Datasets | Yes | We used NASBench-101 and NASBench-201, both cell-based search spaces. NASBench-101 has 423,621 candidates trained on CIFAR-10 for 108 epochs. NASBench-201 includes 15,625 candidates trained on CIFAR-10, CIFAR-100, and Image Net-16-120 for 200 epochs each. We search for a promising architecture based on the mobilenet V3 search space using MOTE, then train and evaluate it on imagenet-1K. |
| Dataset Splits | No | The paper mentions 'training data' and 'test accuracy' (including for early stopping), but does not explicitly provide percentages, counts, or a detailed methodology for train/validation/test dataset splits used in their experiments. |
| Hardware Specification | Yes | Computation was on Tesla V100 GPUs, with MOTE or MOTE-NAS costs calculated specifically on V100. Following 200 epochs of training using 10 GTX 2080Ti GPUs on the imagenet-1K dataset |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer' and 'box-cox transformation', but does not provide specific version numbers for software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | The hyperparameters are batch size 256, epochs 50, learning rate 0.001 with Adam optimizer, and cross-entropy loss function. Then select the best architecture by the early stopping version of the test accuracy. |