A continuous restricted Boltzmann Machine with a hardware-amenable learning algorithm

Hsin Chen; Alan Murray

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A continuous restricted Boltzmann Machine with a hardware-amenable learning algorithm

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A continuous restricted Boltzmann Machine with a hardware-amenable learning algorithm

Hsin Chen and Alan Murray

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.2415 LNCS, pp.358-363

2002

Abstract

This paper proposes a continuous stochastic generative model that offers an improved ability to model analogue data, with a simple and reliable learning algorithm. The architecture forms a continuous restricted Boltzmann Machine, with a novel learning algorithm. The capabilities of the model are demonstrated with both artificial and real data. © Springer-Verlag Berlin Heidelberg 2002.

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Title: A continuous restricted Boltzmann Machine with a hardware-amenable learning algorithm
Creators - without role: Hsin Chen - University of Edinburgh
Alan Murray - University of Edinburgh
Identifiers: 9783540440741; 9957774902506774
Academic Unit: Institute of Electronics Engineering, College of Electrical Engineering and Computer Science, National Tsing Hua University
Language: English
Resource Type: Conference paper
Publication Details: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.2415 LNCS, pp.358-363

A continuous restricted Boltzmann Machine with a hardware-amenable learning algorithm

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