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..
Shaping Noise for Robust Attributions in Neural Stochastic Differential Equations
Authors: Sumit Kumar Jha, Rickard Ewetz, Alvaro Velasquez, Arvind Ramanathan, Susmit Jha9567-9574
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on the Image Net dataset in Table 1 and show that the attributions computed over such Neural SDEs with attribution-driven noise are consistently more robust to input perturbations |
| Researcher Affiliation | Collaboration | 1 Computer Science Department, University of Texas at San Antonio, TX 78249 2 Electrical and Computer Engineering Department, University of Central Florida, Orlando, FL 32816 3 Information Directorate, Air Force Research Laboratory, Rome, NY 13441 4 Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439 5 Computer Science Laboratory, SRI International, Menlo Park, CA, 94709 |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate our approach on the Image Net dataset in Table 1 and show that the attributions computed over such Neural SDEs with attribution-driven noise are consistently more robust to input perturbations |
| Dataset Splits | No | The paper mentions '1,000 random images from the Image Net validation data set' but does not specify the overall training/validation/test split percentages or sample counts for all splits to allow reproduction of the data partitioning. |
| Hardware Specification | Yes | Our stochastic training and attribution analysis were performed on the Image Net benchmark using 8 A100 GPUs with 40GB RAM. |
| Software Dependencies | No | The paper mentions 'Res Net-50 model implemented in Pytorch' but does not specify the version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Image Net training was performed using a learning rate of 0.0001 with a Reduce LROn Plateau scheduler, the noise constant σ = 0.5, and the Adam optimizer. |