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..
Flat Seeking Bayesian Neural Networks
Authors: Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan, Thanh-Toan Do, Dinh Phung, Trung Le
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments by leveraging our sharpness-aware posterior with the state-of-the-art and well-known BNNs... In this section, we conduct various experiments to demonstrate the effectiveness of the sharpness-aware approach on Bayesian Neural networks... Our experimental results, presented in Tables 1, 2, 3 for CIFAR-100 and CIFAR-10 dataset, and Table 4 for the Image Net dataset, indicate a notable improvement across all experiments. |
| Researcher Affiliation | Collaboration | Van-Anh Nguyen1 Tung-Long Vuong1,2 Hoang Phan2,3 Thanh-Toan Do1 Dinh Phung 1,2 Trung Le 1 1Department of Data Science and AI, Monash University, Australia 2Vin AI, Vietnam 3New York University, United States |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation is provided in https://github.com/anh-ntv/flat_bnn.git |
| Open Datasets | Yes | The experiments are conducted on three benchmark datasets: CIFAR-10, CIFAR-100, and Image Net ILSVRC-2012, and report accuracy, negative log-likelihood (NLL), and Expected Calibration Error (ECE) to estimate the calibration capability and uncertainty of our method against baselines. |
| Dataset Splits | No | The paper states 'The details of the dataset and implementation are described in the supplementary material', but does not provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or explicit references to predefined splits) within the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions). |
| Experiment Setup | No | The paper states 'The details of the dataset and implementation are described in the supplementary material' and mentions 'hyper-parameter settings' in the ablation studies ('under the same hyper-parameter settings'). However, concrete hyperparameter values or detailed training configurations are not provided within the main text of the paper. |