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..
SmoothHess: ReLU Network Feature Interactions via Stein's Lemma
Authors: Max Torop, Aria Masoomi, Davin Hill, Kivanc Kose, Stratis Ioannidis, Jennifer Dy
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate the superior flexibility of Smooth Hess to capture interactions on MNIST, FMNIST, and CIFAR10. We utilize Smooth Hess to derive insights into a network trained on a real-world medical spirometry dataset. |
| Researcher Affiliation | Collaboration | 1 Northeastern University, 2 Memorial Sloan Kettering Cancer Center EMAIL, EMAIL, {kosek}@mskcc.org |
| Pseudocode | Yes | Algorithm 1 Joint Smooth Hess and Smooth Grad Estimation |
| Open Source Code | Yes | Our code is publicly available. https://github.com/MaxTorop/SmoothHess |
| Open Datasets | Yes | Experiments were conducted on a real-world spirometry regression dataset, three image datasets (MNIST [45], FMNIST [90] and CIFAR10 [43]), and one synthetic dataset (Four Quadrant). |
| Dataset Splits | Yes | We further split the train set into 50, 000 images for training and 10, 000 for validation. [...] We further split the train set into 40, 000 images for training and 10, 000 for validation. |
| Hardware Specification | Yes | Experiments were performed on an internal cluster using NVIDIA A100 GPUs and AMD EPYC223 7302 16-Core processors. |
| Software Dependencies | No | The paper mentions software like TensorFlow [1] and JAX [13] implicitly through citations for background, and PyTorch [62] for automatic differentiation. However, it does not specify version numbers for these software components or any other libraries/solvers. |
| Experiment Setup | Yes | Training lasted for 40, 000 iterations with a batch size of 128 and a starting learning rate of 1e-3 which was decayed by a factor of 1e-1 at iterations 5000, 10, 000 and 20, 000. |