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..
A PAC-Bayesian Generalization Bound for Equivariant Networks
Authors: Arash Behboodi, Gabriele Cesa, Taco S. Cohen
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In general, the bound indicates that using larger group size in the model improves the generalization error substantiated by extensive numerical experiments. |
| Researcher Affiliation | Collaboration | Arash Behboodi Qualcomm AI Research, Amsterdam EMAIL Gabriele Cesa Qualcomm AI Research, Amsterdam AMLab, University of Amsterdam EMAIL Taco Cohen Qualcomm AI Research, Amsterdam EMAIL |
| Pseudocode | No | The paper describes mathematical derivations and models but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] The code and the data are proprietary |
| Open Datasets | Yes | We have used datasets based on natural images and synthetic data. [...] we perform a larger study on the transformed MNIST datasets |
| Dataset Splits | No | The paper mentions training until a certain margin is reached, but does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] |
| Software Dependencies | No | The paper states that training details were specified but does not provide specific software names with version numbers, such as libraries or frameworks. |
| Experiment Setup | Yes | Models are trained until 99% of the training set is correctly classified with at least a margin γ. We used γ = 10 in the synthetic datasets and γ = 2 in the image ones. |