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..
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Authors: Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward Suh
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our technique to several existing robust training algorithms and achieve a 2.2 speed-up for TRADES and MART on CIFAR-10 and a 1.7 speed-up for Aug Mix on CIFAR-10-C and CIFAR-100-C without any reduction in clean and robust accuracy. 4 Experiments |
| Researcher Affiliation | Collaboration | Weizhe Hua1, Yichi Zhang1, Chuan Guo2, Zhiru Zhang1, G. Edward Suh1,2 1Cornell University, 2Facebook AI Research |
| Pseudocode | Yes | Algorithm 1: Standard robust DNN training algorithm. Algorithm 2: Bullet Train. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We apply our technique to several existing robust training algorithms and achieve a 2.2 speed-up for TRADES and MART on CIFAR-10 and a 1.7 speed-up for Aug Mix on CIFAR-10-C and CIFAR-100-C without any reduction in clean and robust accuracy. MNIST (Le Cun et al., 2010) CIFAR-10 (Krizhevsky, 2009). |
| Dataset Splits | No | The paper mentions 'training set' and 'corrupted test samples' for evaluation, implying train/test splits, but it does not specify exact percentages, sample counts for training, validation, and test splits, nor does it explicitly mention a validation set. |
| Hardware Specification | No | The paper states 'using a single NVIDIA GPU' but does not provide specific model numbers or detailed specifications of the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | When Bullet Train is applied, we set NB = 10 with α = 0.007, NR ∈ [0, 2] with α = 1.7ϵ/NR, NO = 0, and γ = 0.8. Figure 6 shows that the accuracy and theoretical speedup of Bullet Train under different γ ∈ [0.65, 0.9]. |