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..
TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness
Authors: Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin Rubinstein, Ce Zhang, Bo Li
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on TRS and compare with 6 state-of-the-art ensemble baselines against 8 whitebox attacks on different datasets, demonstrating that the proposed TRS outperforms all baselines significantly. |
| Researcher Affiliation | Collaboration | 1 University of Illinois Urbana-Champaign 2 Tencent Inc. 3 University of Melbourne 4 Huazhong University of Science and Technology 5 ETH Zurich |
| Pseudocode | Yes | We present one-epoch training pseudo code in Algorithm 1 of Appendix F. |
| Open Source Code | Yes | The code is publicly available2. 2https://github.com/AI-secure/Transferability-Reduced-Smooth-Ensemble |
| Open Datasets | Yes | We conduct our experiments on widely-used image datasets including hand-written dataset MNIST [29]; and colourful image datasets CIFAR-10 and CIFAR-100 [26]. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about validation splits (e.g., percentages or sample counts) in the main text. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU model, CPU, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | The detailed hyper-parameter setting and training criterion are discussed in Appendix F. |