消除神经网络文献中对 KART 和 UAT 的常见误读

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-29 DOI:arxiv-2408.16389

Vugar Ismailov

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引用次数: 0

摘要

这篇论文讨论了科尔莫哥罗德-阿诺德表征定理（KART）和通用逼近定理（UAT），重点是它们在一些与神经网络逼近相关的论文中常见的错误解释。我们的评论旨在帮助神经网络专家更准确地理解 KART 和 UAT。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Addressing Common Misinterpretations of KART and UAT in Neural Network Literature

This note addresses the Kolmogorov-Arnold Representation Theorem (KART) and the Universal Approximation Theorem (UAT), focusing on their common misinterpretations in some papers related to neural network approximation. Our remarks aim to support a more accurate understanding of KART and UAT among neural network specialists.

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arXiv - CS - Neural and Evolutionary Computing

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