Boltzmann machines with clusters of stochastic binary units

Da Teng; Zhang Li; Guanghong Gong; Liang Han

doi:10.1142/S1793962316500185

Boltzmann machines with clusters of stochastic binary units

Da Teng^*
, Zhang Li
, Guanghong Gong
, Liang Han

^*此作品的通讯作者

Beihang University
Yunnan University

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

The original restricted Boltzmann machines (RBMs) are extended by replacing the binary visible and hidden variables with clusters of binary units, and a new learning algorithm for training deep Boltzmann machine of this new variant is proposed. The sum of binary units of each cluster is approximated by a Gaussian distribution. Experiments demonstrate that the proposed Boltzmann machines can achieve good performance in the MNIST handwritten digital recognition task.

源语言	英语
文章编号	1650018
期刊	International Journal of Modeling, Simulation, and Scientific Computing
卷	7
期	2
DOI	https://doi.org/10.1142/S1793962316500185
出版状态	已出版 - 1 6月 2016

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10.1142/S1793962316500185

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title = "Boltzmann machines with clusters of stochastic binary units",

abstract = "The original restricted Boltzmann machines (RBMs) are extended by replacing the binary visible and hidden variables with clusters of binary units, and a new learning algorithm for training deep Boltzmann machine of this new variant is proposed. The sum of binary units of each cluster is approximated by a Gaussian distribution. Experiments demonstrate that the proposed Boltzmann machines can achieve good performance in the MNIST handwritten digital recognition task.",

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author = "Da Teng and Zhang Li and Guanghong Gong and Liang Han",

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AU - Teng, Da

AU - Li, Zhang

AU - Gong, Guanghong

AU - Han, Liang

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AB - The original restricted Boltzmann machines (RBMs) are extended by replacing the binary visible and hidden variables with clusters of binary units, and a new learning algorithm for training deep Boltzmann machine of this new variant is proposed. The sum of binary units of each cluster is approximated by a Gaussian distribution. Experiments demonstrate that the proposed Boltzmann machines can achieve good performance in the MNIST handwritten digital recognition task.

KW - Restricted Boltzmann machines

KW - machine learning

KW - unsupervised learning

UR - https://www.scopus.com/pages/publications/84973352075

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DO - 10.1142/S1793962316500185

M3 - 文章

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Boltzmann machines with clusters of stochastic binary units

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