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Volumn 5, Issue , 2014, Pages 3800-3809

Neural variational inference and learning in belief networks

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

EID: 84919786239     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (263)

References (26)
  • 2
    • 0030297038 scopus 로고    scopus 로고
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    • Dayan, Peter and Hinton, Geoffrey E. Varieties of helmholtz machine. Neural Networks, 9(8): 1385-1403, 1996.
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    • Dayan, P.1    Hinton, G.E.2
  • 4
    • 84897694817 scopus 로고    scopus 로고
    • Variance reduction techniques for gradient estimates in reinforcement learning
    • Greensmith, Evan, Bartlett, Peter L., and Baxter, Jonathan. Variance reduction techniques for gradient estimates in reinforcement learning. Journal of Machine Learning Research, 5:1471-1530, 2004.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 1471-1530
    • Greensmith, E.1    Bartlett, P.L.2    Baxter, J.3
  • 8
    • 0029652445 scopus 로고
    • The "wake-sleep" algorithm for unsupervised neural networks
    • Hinton, Geoffrey E, Dayan, Peter, Frey, Brendan J, and Neal, Radford M. 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
  • 9
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • Hinton, Geoffrey E., Osindero, Simon, and Teh, Yee Whye. A fast learning algorithm for deep belief nets. Neural Computation, 18(7): 1527-1554, 2006.
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.W.3
  • 10
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • Jordan, Michael I., Ghahramani, Zoubin, Jaakkola, Tommi S., and Saul, Lawrence K. An introduction to variational methods for graphical models. Machine Learning, 37(2): 183-233, 1999.
    • (1999) Machine Learning , vol.37 , Issue.2 , pp. 183-233
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.S.3    Saul, L.K.4
  • 11
    • 70049083257 scopus 로고    scopus 로고
    • Fast inference in sparse coding algorithms with applications to object recognition
    • NYU
    • Kavukcuoglu, Koray, Ranzato, Marc'Aurelio, and LeCun, Yann. Fast inference in sparse coding algorithms with applications to object recognition. Technical report, Courant Institute, NYU, 2008.
    • (2008) Technical Report, Courant Institute
    • Kavukcuoglu, K.1    Ranzato, M.2    Lecun, Y.3
  • 14
    • 84861999538 scopus 로고    scopus 로고
    • The neural autoregressive distribution estimator
    • Larochelle, Hugo and Murray, Iain. The neural autoregressive distribution estimator. JMLR: W&CP, 15:29-37, 2011.
    • (2011) JMLR: W&CP , vol.15 , pp. 29-37
    • Larochelle, H.1    Murray, I.2
  • 15
    • 44049116681 scopus 로고
    • Connectionist learning of belief networks
    • Neal, Radford M. Connectionist learning of belief networks. Artificial intelligence, 56(1):71-113, 1992.
    • (1992) Artificial Intelligence , vol.56 , Issue.1 , pp. 71-113
    • Neal, R.M.1
  • 16
    • 84867133463 scopus 로고    scopus 로고
    • Variational bayesian inference with stochastic search
    • Paisley, John William, Blei, David M., and Jordan, Michael I. Variational bayesian inference with stochastic search. In ICML, 2012.
    • (2012) ICML
    • Paisley, J.W.1    Blei, D.M.2    Jordan, M.I.3
  • 26
    • 0000337576 scopus 로고
    • Simple statistical gradient-following algorithms for connectionist reinforcement learning
    • Williams, Ronald J. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3-4):229-256, 1992.
    • (1992) Machine Learning , vol.8 , Issue.3-4 , pp. 229-256
    • Williams, R.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.