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..
Bayesian Adversarial Learning
Authors: Nanyang Ye, Zhanxing Zhu
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed methods on two commonly used datasets, MNIST and CIFAR-10, and compare with various adversarial learning methods. As shown in Table 1, Bayesian adversarial training consistently achieves higher adversarial accuracy on both MNIST and CIFAR-10 on a variety of attacks. |
| Researcher Affiliation | Collaboration | Yingqian Li1, Xuanqing Liu1, Xingxing Zhang2, Zhuowen Tu1, Bo Li1. 1 University of California, San Diego. 2 Google AI |
| Pseudocode | Yes | Algorithm 1: Bayesian Adversarial Training (BAT) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | We evaluate the proposed methods on two commonly used datasets, MNIST and CIFAR-10, and compare with various adversarial learning methods. |
| Dataset Splits | Yes | For MNIST, we use a 60K training set and 10K test set. For CIFAR-10, we use a 50K training set and 10K test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models or processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | For MNIST, we use a LeNet-5 architecture [22] with 2 convolutional layers and 2 fully connected layers. The training is performed for 100 epochs using Adam optimizer with a learning rate of 0.001. For CIFAR-10, we use a Wide ResNet-28-10 architecture [32]. The training is performed for 200 epochs using Adam optimizer with a learning rate of 0.001. |