BELIEF PROPAGATION AND LEARNING IN CONVOLUTION MULTI-LAYER FACTOR GRAPHS

被引：0

作者：

Palmieri, Francesco A. N. ^{[1
]}

Buonanno, Amedeo ^{[1
]}

机构：

[1] Univ Naples 2, Dipartimento Ingn Ind Informaz, I-81031 Aversa, CE, Italy

来源：

2014 4TH INTERNATIONAL WORKSHOP ON COGNITIVE INFORMATION PROCESSING (CIP) | 2014年

关键词：

Belief Propagation; Factor Graphs; Deep Belief Graphs;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In modeling time series, convolution multi-layer graphs are able to capture long-term dependence at a gradually increasing scale. We present an approach to learn a layered factor graph architecture starting from a stationary latent models for each layer. Simulations of belief propagation are reported for a three-layer graph on a small data set of characters.

引用

页数：6

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