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 [1].
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. |