Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Adversarial Neural Pruning with Latent Vulnerability Suppression
Authors: Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 21,276 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,666 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our Adversarial Neural Pruning with Vulnerability Suppression (ANP-VS) method on multiple benchmark datasets, on which it not only obtains state-of-the-art adversarial robustness but also improves the performance on clean examples, using only a fraction of the parameters used by the full network. |
| Researcher Affiliation | Collaboration | 1School of Computing, KAIST, South Korea 2School of Electrical Engineering, KAIST, South Korea 3Graduate School of AI, KAIST, South Korea 4AITRICS, South Korea. |
| Pseudocode | Yes | Algorithm 1 Adversarial training by ANP-VS |
| Open Source Code | Yes | the code is available online 2. 2https://github.com/divyam3897/ANP_VS |
| Open Datasets | Yes | 1. MNIST. This dataset (Le Cun, 1998)... 2. CIFAR-10. This dataset (Krizhevsky, 2012)... 3. CIFAR-100. This dataset (Krizhevsky, 2012) |
| Dataset Splits | No | The paper mentions 'training instances' and 'test instances' for the datasets, but does not explicitly provide details for a separate validation split used in their experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'Le Net 5-Caffe', 'PyTorch', and 'PGD' but does not specify their version numbers. |
| Experiment Setup | Yes | For MNIST, we consider a Lenet-5-Caffe model with a perturbation radius of ε = 0.3, perturbation per step of 0.01, 20 PGD steps for training, and 40 PGD steps with random restarts for evaluating the trained model. ... We use ε = 0.03, 10 PGD steps for training and 40 steps with random restart for evaluation. |