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..
On the universality of deep learning
Authors: Emmanuel Abbe, Colin Sandon
NeurIPS 2020 | Venue PDF | 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. |