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Chaotic Boltzmann machines

The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, effici...

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Detalles Bibliográficos
Autores principales: Suzuki, Hideyuki, Imura, Jun-ichi, Horio, Yoshihiko, Aihara, Kazuyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3617428/
https://www.ncbi.nlm.nih.gov/pubmed/23558425
http://dx.doi.org/10.1038/srep01610
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author Suzuki, Hideyuki
Imura, Jun-ichi
Horio, Yoshihiko
Aihara, Kazuyuki
author_facet Suzuki, Hideyuki
Imura, Jun-ichi
Horio, Yoshihiko
Aihara, Kazuyuki
author_sort Suzuki, Hideyuki
collection PubMed
description The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented.
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spelling pubmed-36174282013-04-05 Chaotic Boltzmann machines Suzuki, Hideyuki Imura, Jun-ichi Horio, Yoshihiko Aihara, Kazuyuki Sci Rep Article The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented. Nature Publishing Group 2013-04-05 /pmc/articles/PMC3617428/ /pubmed/23558425 http://dx.doi.org/10.1038/srep01610 Text en Copyright © 2013, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by/3.0/ This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/
spellingShingle Article
Suzuki, Hideyuki
Imura, Jun-ichi
Horio, Yoshihiko
Aihara, Kazuyuki
Chaotic Boltzmann machines
title Chaotic Boltzmann machines
title_full Chaotic Boltzmann machines
title_fullStr Chaotic Boltzmann machines
title_full_unstemmed Chaotic Boltzmann machines
title_short Chaotic Boltzmann machines
title_sort chaotic boltzmann machines
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3617428/
https://www.ncbi.nlm.nih.gov/pubmed/23558425
http://dx.doi.org/10.1038/srep01610
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