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Volumn 382, Issue , 2009, Pages

Using fast weights to improve persistent contrastive divergence

Author keywords

[No Author keywords available]

Indexed keywords

CONTRASTIVE DIVERGENCE; DATA POINTS; ENERGY LANDSCAPE; EQUILIBRIUM DISTRIBUTIONS; MARKOV CHAIN; RESTRICTED BOLTZMANN MACHINE; SUFFICIENT STATISTICS; VARIANCE ESTIMATE; WEIGHT UPDATE;

EID: 70049098965     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1553374.1553506     Document Type: Conference Paper
Times cited : (38)

References (15)
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    • Bharath, B.1    Borkar, V.2
  • 2
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    • Taboo Search: An Approach to the Multiple Minima Problem
    • Cvijovi, D., & Klinowski, J. (1995). Taboo Search: An Approach to the Multiple Minima Problem. Science, 267, 664-666.
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    • Cvijovi, D.1    Klinowski, J.2
  • 3
    • 0013344078 scopus 로고    scopus 로고
    • Training Products of Experts by Minimizing Contrastive Divergence
    • Hinton, G. (2002). Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, 14, 1771-1800.
    • (2002) Neural Computation , vol.14 , pp. 1771-1800
    • Hinton, G.1
  • 4
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • Hinton, G., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527-1554.
    • (2006) Neural Computation , vol.18 , pp. 1527-1554
    • Hinton, G.1    Osindero, S.2    Teh, Y.3
  • 5
    • 0033225865 scopus 로고    scopus 로고
    • An Introduction to Variational Methods for Graphical Models
    • Jordan, M., Ghahramani, Z., Jaakkola, T., & Saul, L.(1999). An Introduction to Variational Methods for Graphical Models. Machine Learning, 37, 183-233.
    • (1999) Machine Learning , vol.37 , pp. 183-233
    • Jordan, M.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.4
  • 9
    • 44049116681 scopus 로고
    • Connectionist learning of belief networks
    • Neal, R. (1992). Connectionist learning of belief networks. Artificial Intelligence, 56, 71-113.
    • (1992) Artificial Intelligence , vol.56 , pp. 71-113
    • Neal, R.1
  • 10
    • 0002788893 scopus 로고    scopus 로고
    • A view of the EM algorithm that justifies incremental, sparse, and other variants
    • Neal, R., & Hinton, G. (1998). A view of the EM algorithm that justifies incremental, sparse, and other variants. Learning in Graphical Models, 89, 355-368.
    • (1998) Learning in Graphical Models , vol.89 , pp. 355-368
    • Neal, R.1    Hinton, G.2
  • 12
    • 70049107352 scopus 로고    scopus 로고
    • Generalized darting monte carlo
    • CSRG-478, University of Toronto, Department of Computer Science
    • Sminchisescu, C., & Welling, M. (2007). Generalized darting monte carlo (Technical Report CSRG-478). University of Toronto, Department of Computer Science.
    • (2007) Technical Report
    • Sminchisescu, C.1    Welling, M.2
  • 14
    • 56449086223 scopus 로고    scopus 로고
    • Ti1eleman, T. (2008). Training restricted Boltzmann machines using approximations to the likelihood gradient. Proceedings of the 25th international conference on Machine learning (pp. 1064-1071).
    • Ti1eleman, T. (2008). Training restricted Boltzmann machines using approximations to the likelihood gradient. Proceedings of the 25th international conference on Machine learning (pp. 1064-1071).


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.