Path Choice Matters for Clear Attributions in Path Methods
Authors: Borui Zhang, Wenzhao Zheng, Jie Zhou, Jiwen Lu
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct qualitative and quantitative experiments to demonstrate the superiority of our proposed SAMP method. |
| Researcher Affiliation | Academia | Borui Zhang , Wenzhao Zheng , Jie Zhou , Jiwen Lu Department of Automation, Tsinghua University, China {zhang-br21, zhengwz18}@mails.tsinghua.edu.cn; {jzhou, lujiwen}@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1: The SAMP++ algorithm. |
| Open Source Code | Yes | Corresponding author. 1Code: https://github.com/zbr17/SAMP |
| Open Datasets | Yes | We evaluate SAMP on the widely used MNIST (Deng, 2012), CIFAR-10 (Krizhevsky et al., 2009), and Image Net (Deng et al., 2009). |
| Dataset Splits | No | The paper mentions training models and using test sets, but does not explicitly provide details about specific training/validation/test dataset splits (e.g., percentages or counts) or reference predefined splits with citations for reproducibility beyond stating the datasets used. |
| Hardware Specification | Yes | We perform all experiments with Py Torch on one NVIDIA 3090 card. |
| Software Dependencies | No | The paper mentions 'Py Torch torchvision package' and 'Adam W optimizer' but does not specify exact version numbers for these software dependencies to ensure reproducibility. |
| Experiment Setup | Yes | If without special specifications, we fix the step size s in SAMP as 224 16 for Image Net and 10 for other datasets, the ratio of the infinitesimal upper bound η to x 1 as 0.1, and the momentum coefficient λ as 0.5. |