Temperature-Based Deep Boltzmann Machines-Reference-Cited by-同舟云学术

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Temperature-Based Deep Boltzmann Machines

Published:2017-09-08 Issue:1 Volume:48 Page:95-107
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Passos Leandro Aparecido,Papa João Paulo

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Conselho Nacional de Desenvolvimento Cientìfico e Tecnològico

Coordenação de Aperfeiçoamento de Pessoal de Nìvel Superior

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

http://link.springer.com/article/10.1007/s11063-017-9707-2/fulltext.html

Reference35 articles.

1. Bekenstein JD (1973) Black holes and entropy. Phys Rev D 7(8):2333

2. Beraldo e Silva L, Lima M, Sodré L, Perez J (2014) Statistical mechanics of self-gravitating systems: mixing as a criterion for indistinguishability. Phys Rev D 90(12):123004

3. Duong CN, Luu K, Quach KG, Bui TD (2015) Beyond principal components: deep Boltzmann machines for face modeling. In: 2015 IEEE conference on computer vision and pattern recognition, CVPR’15, pp 4786–4794

4. Gadjiev B, Progulova T (2015) Origin of generalized entropies and generalized statistical mechanics for superstatistical multifractal systems. In: International workshop on Bayesian inference and maximum entropy methods in science and engineering, vol 1641, pp 595–602

5. Goh H, Thome N, Cord M, Lim JH (2012) Unsupervised and Supervised Visual Codes with Restricted Boltzmann Machines. In: Proceedings of computer vision—ECCV 2012: 12th European conference on computer vision, Florence, Italy, October 7–13, 2012, Part V. Springer, Berlin, pp 298–311. doi: 10.1007/978-3-642-33715-4_22

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