Global Universality of the Two-Layer Neural Network with the -Rectified Linear Unit

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-01-18 DOI:10.1155/2024/3262798

Naoya Hatano, Masahiro Ikeda, Isao Ishikawa, Yoshihiro Sawano

引用次数: 0

Abstract

This paper concerns the universality of the two-layer neural network with the -rectified linear unit activation function with with a suitable norm without any restriction on the shape of the domain in the real line. This type of result is called global universality, which extends the previous result for by the present authors. This paper covers -sigmoidal functions as an application of the fundamental result on -rectified linear unit functions.

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具有 "整流线性单元 "的双层神经网络的全局通用性

本文论述了具有适当规范的-修正线性单元激活函数的双层神经网络的普遍性，对实线域的形状没有任何限制。这类结果被称为全局普遍性，它扩展了本文作者之前的结果。本文涵盖了 "正弦函数"，作为 "修正线性单元函数 "基本结果的应用。

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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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.