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..
Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
Authors: Matthew Farrugia-Roberts
NeurIPS 2023 | Venue PDF | 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 EMAIL |
| 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. |