Brauer’s Group Equivariant Neural Networks
Authors: Edward Pearce-Crump
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we take an entirely different approach, one which results in a full characterisation of all of the possible group equivariant neural networks whose layers are some tensor power of Rn for the following three symmetry groups: O(n), the orthogonal group; SO(n), the special orthogonal group; and Sp(n), the symplectic group. |
| Researcher Affiliation | Academia | Department of Computing, Imperial College London, United Kingdom. Correspondence to: Edward Pearce Crump <ep1011@ic.ac.uk>. |
| Pseudocode | No | The paper contains mathematical derivations and matrix examples, but no clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve training on datasets; therefore, no public dataset information is provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset validation splits; therefore, no split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that require specific hardware; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical contributions and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | As a theoretical paper, it does not detail any experimental setup, hyperparameters, or system-level training settings. |