Hopfield Networks for Vector Quantization

被引：4

作者：

Bauckhage, C. ^{[1
,2
,3
]}

Ramamurthy, R. ^{[1
,3
]}

Sifa, R. ^{[1
,3
]}

机构：

[1] Fraunhofer Ctr Machine Learning, St Augustin, Germany

[2] Univ Bonn, Comp Sci, Bonn, Germany

[3] Fraunhofer IAIS, St Augustin, Germany

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II | 2020年 / 12397卷

关键词：

D O I：

10.1007/978-3-030-61616-8_16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider the problem of finding representative prototypes within a set of data and solve it using Hopfield networks. Our key idea is to minimize the mean discrepancy between kernel density estimates of the distributions of data points and prototypes. We show that this objective can be cast as a quadratic unconstrained binary optimization problem which is equivalent to a Hopfield energy minimization problem. This result is of current interest as it suggests that vector quantization can be accomplished via adiabatic quantum computing.

引用

页码：192 / 203

页数：12

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