Local Identifiability of Deep ReLU Neural Networks: the Theory
Authors: Joachim Bona-Pellissier, François Malgouyres, Francois Bachoc
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We did not run experiments. |
| Researcher Affiliation | Academia | Institut de Mathématiques de Toulouse ; UMR 5219 Université de Toulouse ; CNRS a UT1, F-31042 Toulouse, France b UPS, F-31062 Toulouse Cedex 9, France |
| Pseudocode | No | The paper provides mathematical derivations and theorems but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states, 'We did not run experiments.', indicating no source code for a methodology to be released. |
| Open Datasets | No | The paper explicitly states, 'We did not run experiments.', therefore no datasets are used for training. |
| Dataset Splits | No | The paper explicitly states, 'We did not run experiments.', therefore no validation sets or splits are mentioned. |
| Hardware Specification | No | The paper explicitly states, 'We did not run experiments.', therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper explicitly states, 'We did not run experiments.', therefore no software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper explicitly states, 'We did not run experiments.', therefore no specific experimental setup details like hyperparameters or training configurations are provided. |