A PAC-Bayesian Generalization Bound for Equivariant Networks
Authors: Arash Behboodi, Gabriele Cesa, Taco S. Cohen
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 behboodi@qti.qualcomm.com Gabriele Cesa Qualcomm AI Research, Amsterdam AMLab, University of Amsterdam gcesa@qti.qualcomm.com Taco Cohen Qualcomm AI Research, Amsterdam tacos@qti.qualcomm.com |
| 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. |