Achieve the Minimum Width of Neural Networks for Universal Approximation
Authors: Yongqiang Cai
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 caiyq.math@bnu.edu.cn 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. |