On the universality of deep learning
Authors: Emmanuel Abbe, Colin Sandon
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We do not anticipate any ethical aspects and future societal consequences due to the theoretical focus of this work. |
| Researcher Affiliation | Academia | Emmanuel Abbe Mathematics Institute EPFL Lausanne, 1005 Switzerland Colin Sandon Department of Mathematics MIT Cambridge, MA 02139 |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | No explicit statement or link providing access to source code for the described methodology was found. |
| Open Datasets | No | This is a theoretical paper and does not utilize publicly available datasets for training empirical models. |
| Dataset Splits | No | This is a theoretical paper and does not provide specific details on dataset splits for empirical reproduction. |
| Hardware Specification | No | This is a theoretical paper and does not mention specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not provide details on experimental setup or hyperparameters. |