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..
Improving Bayesian Neural Networks by Adversarial Sampling
Authors: Jiaru Zhang, Yang Hua, Tao Song, Hao Wang, Zhengui Xue, Ruhui Ma, Haibing Guan10110-10117
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments with multiple network structures on different datasets, e.g., CIFAR-10 and CIFAR-100. Experimental results validate the correctness of the theoretical analysis and the effectiveness of the Adversarial Sampling on improving model performance. |
| Researcher Affiliation | Academia | 1 Shanghai Jiao Tong University 2 Queen s University Belfast 3 Louisiana State University |
| Pseudocode | Yes | Algorithm 1: Training with Adversarial Sampling |
| Open Source Code | Yes | We release our codes at https://github.com/AISIGSJTU/AS. |
| Open Datasets | Yes | We train a variety of Bayesian neural networks, including Res Net20, Res Net56 (He et al. 2016), and VGG (Simonyan and Zisserman 2015), on CIFAR-10, and CIFAR-100 datasets (Krizhevsky 2009). |
| Dataset Splits | No | The paper mentions training on CIFAR-10 and CIFAR-100 datasets and evaluating on test sets, but does not explicitly provide details about training/validation/test splits (e.g., percentages, sample counts, or specific cross-validation setup). |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | Yes | For simplicity, we set the hyperparameter α = 0.02 and N = 5 on models trained with Adversarial Sampling. |