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..
Path Choice Matters for Clear Attributions in Path Methods
Authors: Borui Zhang, Wenzhao Zheng, Jie Zhou, Jiwen Lu
ICLR 2024 | Venue PDF | 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 EMAIL; EMAIL |
| 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. |