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Volumn , Issue , 2013, Pages 18-22

Contrastive divergence learning for the Restricted Boltzmann Machine

Author keywords

contrastive divergence learning; markov chain; restricted boltzmann machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; EXPERIMENTS; MARKOV PROCESSES;

EID: 84901790749     PISSN: 21579555     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNC.2013.6817936     Document Type: Conference Paper
Times cited : (11)

References (14)
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  • 2
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  • 3
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    • Desjardins, G.1    Courville, A.2    Bengio, Y.3    Vincent, P.4    Delalleau, O.5
  • 4
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    • Tempered markov chain monte carlo for training of restricted Boltzmann Machines
    • Desjardins, G. , Courville, A. , Bengio, Y. , Vincent, P. and Delalleau, O. Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines. Neural Computation, vol. 20, pp. 1634-1678, 2009.
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    • Desjardins, G.1    Courville, A.2    Bengio, Y.3    Vincent, P.4    Delalleau, O.5
  • 5
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    • G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, vol. 14, pp. 1771-1800, 2002.
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    • Hinton, G.E.1
  • 8
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    • A tutorial on energy-based learning
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    • Y. LeCun, S. Chopra, R. M. Hadsell, M. -A. Ranzato, and F. -J. Huang. A tutorial on energy-based learning. in Predicting Structured Data, pp. 191-246, G. Bakir and T. Hofman and B. Scholkopf and A. Smola and B. Taskar:MIT Press, 2006.
    • (2006) Predicting Structured Data , pp. 191-246
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  • 11
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    • Exponential family harmoniums with an application to information retrieval
    • Cambridge, MA MIT Press
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  • 14
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    • Justifying and generalizing contrastive divergence
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.