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Volumn , Issue , 2010, Pages 495-500

Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives

Author keywords

[No Author keywords available]

Indexed keywords

BIASED APPROXIMATION; BUILDING BLOCKES; CONTRASTIVE DIVERGENCE; LEARNING PROCESS; LOG LIKELIHOOD; OPTIMIZATION TECHNIQUES; RESILIENT BACKPROPAGATION; RESTRICTED BOLTZMANN MACHINE;

EID: 84887100846     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (6)

References (13)
  • 1
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    • Information processing in dynamical systems: Foundations of harmony theory
    • D. E. Rumelhart and J. L. McClelland, editors, MIT Press
    • P. Smolensky. Information processing in dynamical systems: Foundations of harmony theory. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: Foundations, pages 194-281. MIT Press, 1986.
    • (1986) Parallel Distributed Processing: Explorations In the Microstructure of Cognition , vol.1 , pp. 194-281
    • Smolensky, P.1
  • 3
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14:1771-1800, 2002.
    • (2002) Neural Computation , vol.14 , pp. 1771-1800
    • Hinton, G.E.1
  • 5
    • 67651049775 scopus 로고    scopus 로고
    • Justifying and generalizing contrastive divergence
    • Y. Bengio and O. Delalleau. Justifying and generalizing contrastive divergence. Neural Computation, 21(6):1601-1621, 2009.
    • (2009) Neural Computation , vol.21 , Issue.6 , pp. 1601-1621
    • Bengio, Y.1    Delalleau, O.2
  • 6
    • 84887083624 scopus 로고    scopus 로고
    • Bounding the bias of contrastive divergence learning
    • In press
    • A. Fischer and C. Igel. Bounding the bias of contrastive divergence learning. Neural Computation. In press.
    • Neural Computation
    • Fischer, A.1    Igel, C.2
  • 7
    • 78049370715 scopus 로고    scopus 로고
    • Contrastive divergence learning may diverge when training restricted Boltzmann machines. Frontiers in Computational Neuroscience
    • BCCN 2009
    • A Fischer and C. Igel. Contrastive divergence learning may diverge when training restricted Boltzmann machines. Frontiers in Computational Neuroscience. Bernstein Conference on Computational Neuroscience (BCCN 2009), 2009.
    • (2009) Bernstein Conference On Computational Neuroscience
    • Fischer, A.1    Igel, C.2
  • 9
    • 78049383425 scopus 로고    scopus 로고
    • Empirical analysis of the divergence of Gibbs sampling based learning algorithms for Restricted Boltzmann Machines
    • LNCS. Springer-Verlag
    • A. Fischer and C. Igel. Empirical analysis of the divergence of Gibbs sampling based learning algorithms for Restricted Boltzmann Machines. In International Conference on Artificial Neural Networks (ICANN 2010), LNCS. Springer-Verlag, 2010.
    • (2010) International Conference On Artificial Neural Networks (ICANN 2010)
    • Fischer, A.1    Igel, C.2
  • 10
    • 0028466750 scopus 로고
    • Advanced supervised learning in multi-layer perceptrons - From backpropagation to adaptive learning algorithms
    • M. Riedmiller. Advanced supervised learning in multi-layer perceptrons - From backpropagation to adaptive learning algorithms. Computer Standards and Interfaces, 16(5):265-278, 1994.
    • (1994) Computer Standards and Interfaces , vol.16 , Issue.5 , pp. 265-278
    • Riedmiller, M.1
  • 11
    • 0037238922 scopus 로고    scopus 로고
    • Empirical evaluation of the improved Rprop learning algorithm
    • C. Igel and M. Hüsken. Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing, 50(C):105-123, 2003.
    • (2003) Neurocomputing , vol.50 , Issue.C , pp. 105-123
    • Igel, C.1    Hüsken, M.2
  • 13
    • 84887063061 scopus 로고    scopus 로고
    • Challenges in training Restricted Boltzmann Machines
    • B. Hammer and T. Villmann, editors, number 04/2010 in Machine Learning Reports
    • A. Fischer and C. Igel. Challenges in training Restricted Boltzmann Machines. In B. Hammer and T. Villmann, editors, New Challenges in Neural Computation (NC2), number 04/2010 in Machine Learning Reports, pages 11-24. 2010.
    • (2010) New Challenges In Neural Computation (NC2) , pp. 11-24
    • Fischer, A.1    Igel, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.