Decreasing the Size of the Restricted Boltzmann Machine

被引：1
|
作者：
Saito, Yohei [1 ]
Kato, Takuya [2 ]
机构：
[1] Univ Tokyo, Inst Ind Sci, Meguro Ku, Tokyo 1538505, Japan
[2] Univ Tokyo, Grad Sch Informat Sci & Technol, Dept Math informat, Bunkyo Ku, Tokyo 1138654, Japan
来源：
NEURAL COMPUTATION | 2019年 / 31卷 / 04期
关键词：
D O I：
10.1162/neco_a_01176
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this letter, we propose a method to decrease the number of hidden units of the restricted Boltzmann machine while avoiding a decrease in the performance quantified by the Kullback-Leibler divergence. Our algorithm is then demonstrated by numerical simulations.
引用
收藏
页码：784 / 805
页数：22
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