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..
Achieve the Minimum Width of Neural Networks for Universal Approximation
Authors: Yongqiang Cai
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that both C-UAP and Lp-UAP for functions on compact domains share a universal lower bound of the minimal width; that is, w min = max(dx, dy). |
| Researcher Affiliation | Academia | Yongqiang Cai Beijing Normal University EMAIL School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, MOE, Beijing Normal University, 100875 Beijing, China |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper is a theoretical work focusing on mathematical proofs and constructions, and therefore does not provide any associated source code. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs rather than empirical evaluation using datasets, so no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation using dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications or computational resources used. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical concepts, therefore it does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup, including hyperparameters or training configurations. |