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Volumn , Issue , 2013, Pages

Learning stochastic feedforward neural networks

Author keywords

[No Author keywords available]

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; NETWORK LAYERS;

EID: 84898947294     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (116)

References (23)
  • 1
    • 0004113976 scopus 로고
    • Technical Report NCRG/94/004 Aston University
    • C. M. Bishop. Mixture density networks. Technical Report NCRG/94/004, Aston University, 1994.
    • (1994) Mixture Density Networks
    • Bishop, C.M.1
  • 5
    • 34548634906 scopus 로고    scopus 로고
    • Gaussian fields for approximate inference in layered sigmoid belief networks
    • Sara A. Solla, Todd K. Leen, and Klaus-Robert Müller, editors, The MIT Press
    • David Barber and Peter Sollich. Gaussian fields for approximate inference in layered sigmoid belief networks. In Sara A. Solla, Todd K. Leen, and Klaus-Robert Müller, editors, NIPS, pages 393-399. The MIT Press, 1999.
    • (1999) NIPS , pp. 393-399
    • Barber, D.1    Sollich, P.2
  • 6
    • 85157999846 scopus 로고    scopus 로고
    • Modeling human motion using binary latent variables
    • G. Taylor, G. E. Hinton, and S. Roweis. Modeling human motion using binary latent variables. In NIPS, 2006.
    • (2006) NIPS
    • Taylor, G.1    Hinton, G.E.2    Roweis, S.3
  • 12
    • 0002788893 scopus 로고    scopus 로고
    • A new view of the em algorithm that justifies incremental, sparse and other variants
    • M. I. Jordan, editor
    • R. M. Neal and G. E. Hinton. A new view of the EM algorithm that justifies incremental, sparse and other variants. In M. I. Jordan, editor, Learning in Graphical Models, pages 355-368. 1998.
    • (1998) Learning in Graphical Models , pp. 355-355
    • Neal, R.M.1    Hinton, G.E.2
  • 13
    • 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
  • 14
    • 0000273048 scopus 로고    scopus 로고
    • Annealed importance sampling
    • R. M. Neal. Annealed importance sampling. Statistics and Computing, 11:125-139, 2001.
    • (2001) Statistics and Computing , vol.11 , pp. 125-139
    • Neal, R.M.1
  • 18
    • 4544294260 scopus 로고    scopus 로고
    • NETLAB: Algorithms for pattern recognitions
    • Springer-Verlag
    • Ian Nabney. NETLAB: algorithms for pattern recognitions. Advances in pattern recognition. Springer-Verlag, 2002.
    • (2002) Advances in Pattern Recognition
    • Nabney, I.1
  • 19
    • 78149306047 scopus 로고    scopus 로고
    • 3-D object recognition with deep belief nets
    • V. Nair and G. E. Hinton. 3-D object recognition with deep belief nets. In NIPS 22, 2009.
    • (2009) NIPS , vol.22
    • Nair, V.1    Hinton, G.E.2
  • 21
    • 84948984184 scopus 로고    scopus 로고
    • Class-specific, top-down segmentation
    • Eran Borenstein and Shimon Ullman. Class-specific, top-down segmentation. In In ECCV, pages 109-124, 2002.
    • (2002) ECCV , pp. 109-124
    • Borenstein, E.1    Ullman, S.2
  • 22
    • 0029652445 scopus 로고
    • The wake-sleep algorithm for unsupervised neural networks
    • G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal. The wake-sleep algorithm for unsupervised neural networks. Science, 268(5214):1158-1161, 1995.
    • (1995) Science , vol.268 , Issue.5214 , pp. 1158-1161
    • Hinton, G.E.1    Dayan, P.2    Frey, B.J.3    Neal, R.M.4
  • 23
    • 84862293074 scopus 로고    scopus 로고
    • Efficient learning of deep boltzmann machines
    • R. Salakhutdinov and H. Larochelle. Efficient learning of deep boltzmann machines. AISTATS, 2010.
    • (2010) AISTATS
    • Salakhutdinov, R.1    Larochelle, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.