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Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks.

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第一作者： Linyan,Gu

第一单位： Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. Electronic address: ly.gu@siat.ac.cn.

作者： Linyan,Gu ^[1] ; Lihua,Yang ^[2] ; Feng,Zhou ^[3]

作者单位： Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. Electronic address: ly.gu@siat.ac.cn. ^[1] School of Mathematics, Sun Yat-sen University, Guangzhou, 510275, China; Guangdong Province Key Laboratory of Computational Science, Guangzhou, 510275, China. Electronic address: mcsylh@mail.sysu.edu.cn. ^[2] School of Information Science, Guangdong University of Finance and Economics, Guangzhou, 510320, China. Electronic address: fengzhou@gdufe.edu.cn. ^[3]

关键词 Approximation theory Feedforward neural network Gaussian-binary deep belief networks Gaussian-binary restricted Boltzmann machines

主题词计算机模拟(Computer Simulation);正态分布(Normal Distribution);概率(Probability)

DOI 10.1016/j.neunet.2022.05.020

PMID 35700559

发布时间 2022-08-02

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