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..
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
Authors: Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James Kwok, Tong Zhang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted to verify the effectiveness of our method over competing algorithms.1 |
| Researcher Affiliation | Collaboration | Han Shi1 , Renjie Pi2 , Hang Xu3, Zhenguo Li3, James T. Kwok1, Tong Zhang1 1Hong Kong University of Science and Technology, Hong Kong EMAIL, EMAIL 2The University of Hong Kong, Hong Kong EMAIL 3Huawei Noah s Ark Lab EMAIL |
| Pseudocode | Yes | Algorithm 1 Generic BO procedure for NAS. and Algorithm 2 BONAS. |
| Open Source Code | Yes | 1The code is available at https://github.com/pipilurj/BONAS. |
| Open Datasets | Yes | In the following experiments, we use NAS-Bench-101 [38], which is the largest NAS benchmark data set (with 423K convolutional architectures), and the more recent NAS-Bench-201 [8], which uses a different search space (with 15K architectures) and is applicable to almost any NAS algorithm. and a new NAS benchmark data set LSTM-12K we recently collected for LSTMs. and on the Penn Tree Bank data set [21] and CIFAR-10 data set. and Image Net [6]. |
| Dataset Splits | Yes | For each data set, we use 85% of the data for training, 10% for validation, and the rest for testing. |
| Hardware Specification | Yes | All experiments are performed on NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions software like 'Adam optimizer' but does not provide specific version numbers for any key software components or libraries. |
| Experiment Setup | Yes | The GCN has four hidden layers with 64 units each. Training is performed by minimizing the square loss, using the Adam optimizer [12] with a learning rate of 0.001 and a mini-batch size of 128. and In step 10 of Algorithm 2, k = 100 models are merged to a super-network and trained for 100 epochs using the procedure discussed in Section 3.2. |