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..
On the VC dimension of deep group convolutional neural networks
Authors: Anna Sepliarskaia, Sophie Langer, Johannes Schmidt-Hieber
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we analyze the generalization capabilities of Group Convolutional Neural Networks (GCNNs) with Re LU activation function through the lens of Vapnik-Chervonenkis (VC) dimension theory. By deriving upper and lower bounds, we investigate how the network architecture affects the VC dimension. The paper does not include experiment. |
| Researcher Affiliation | Collaboration | Anna Sepliarskaia Booking.com Department of Applied Mathematics, University of Twente Drienerlolaan 5 7522 NB Enschede, Netherlands EMAIL Sophie Langer Faculty of Mathematics Ruhr University Bochum Universitätsstraße 150 44801 Bochum, Germany EMAIL Johannes Schmidt-Hieber Department of Applied Mathematics University of Twente Drienerlolaan 5 7522 NB Enschede, Netherlands EMAIL |
| Pseudocode | No | The paper describes methods and proofs in prose and mathematical notation but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks with structured, code-like steps. |
| Open Source Code | No | The paper does not include experiments requiring code |
| Open Datasets | No | The paper does not include experiments. |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments requiring code |
| Experiment Setup | No | The paper does not include experiments. |