Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
Authors: Matthew Farrugia-Roberts
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study functional equivalence classes for single-hidden-layer networks with the hyperbolic tangent nonlinearity, building on the foundational work of Sussmann (1992) on reducibility in this setting. We offer the following theoretical contributions. |
| Researcher Affiliation | Academia | Matthew Farrugia-Roberts School of Computing and Information Systems The University of Melbourne matthew@far.in.net |
| Pseudocode | Yes | Algorithm 4.1 (Parameter canonicalisation). Given a parameter space Wh, proceed: |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is purely theoretical and does not describe any experiments involving datasets, training, or public data availability. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental setup with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational implementation or experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or training configurations. |