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Volumn 3, Issue , 2016, Pages 1937-1946

Factored temporal sigmoid belief networks for sequence learning

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; BLENDING; LEARNING ALGORITHMS; LEARNING SYSTEMS; SUPERVISED LEARNING;

EID: 84998827890     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (8)

References (35)
  • 2
    • 84867129058 scopus 로고    scopus 로고
    • Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription
    • Boulanger-Lewandowski, N., Bengio, Y., and Vincent, P. Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In ICML, 2012.
    • (2012) ICML
    • Boulanger-Lewandowski, N.1    Bengio, Y.2    Vincent, P.3
  • 3
  • 7
    • 84970024465 scopus 로고    scopus 로고
    • Scalable deep poisson factor analysis for topic modeling
    • Gan, Z., Chen, C, Henao, R., Carlson, D., and Carin, L. Scalable deep poisson factor analysis for topic modeling. In ICML, 2015a.
    • (2015) ICML
    • Gan, Z.1    Chen, C.2    Henao, R.3    Carlson, D.4    Carin, L.5
  • 8
    • 84965104862 scopus 로고    scopus 로고
    • Learning deep sigmoid belief networks with data augmentation
    • Gan, Z., Henao, R., Carlson, D., and Carin, L. Learning deep sigmoid belief networks with data augmentation. In AISTATS, 2015b.
    • (2015) AISTATS
    • Gan, Z.1    Henao, R.2    Carlson, D.3    Carin, L.4
  • 9
    • 84965123118 scopus 로고    scopus 로고
    • Deep temporal sigmoid belief networks for sequence modeling
    • Gan, Z., Li, C, Henao, R., Carlson, D., and Carin, L. Deep temporal sigmoid belief networks for sequence modeling. In NIPS, 2015c.
    • (2015) NIPS
    • Gan, Z.1    Li, C.2    Henao, R.3    Carlson, D.4    Carin, L.5
  • 11
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • Hinton, G. E. Training products of experts by minimizing contrastive divergence. In Neural computation, 2002.
    • (2002) Neural Computation
    • Hinton, G.E.1
  • 12
    • 0029652445 scopus 로고
    • The "wake-sleep" algorithm for unsupervised neural networks
    • Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M. The "wake-sleep" algorithm for unsupervised neural networks. In Science, 1995.
    • (1995) Science
    • Hinton, G.E.1    Dayan, P.2    Frey, B.J.3    Neal, R.M.4
  • 15
    • 85083952489 scopus 로고    scopus 로고
    • Auto-encoding variational bayes
    • Kingma, D. P. and Welling, M. Auto-encoding variational bayes. In ICLR, 2014.
    • (2014) ICLR
    • Kingma, D.P.1    Welling, M.2
  • 16
  • 18
    • 84937834963 scopus 로고    scopus 로고
    • A multiplicative model for learning distributed text-based attribute representations
    • Kiros, R., Zemel, R., and Salakhutdinov, R. R. A multiplicative model for learning distributed text-based attribute representations. In NIPS, 2014b.
    • (2014) NIPS
    • Kiros, R.1    Zemel, R.2    Salakhutdinov, R.R.3
  • 19
    • 84919829999 scopus 로고    scopus 로고
    • Distributed representations of sentences and documents
    • Le, Q. V. and Mikolov, T. Distributed representations of sentences and documents. In ICML, 2014.
    • (2014) ICML
    • Le, Q.V.1    Mikolov, T.2
  • 20
    • 84965165777 scopus 로고    scopus 로고
    • Max-margin deep generative models
    • Li, C, Zhu, J., Shi, T., and Zhang, Bo. Max-margin deep generative models. In NIPS, 2015.
    • (2015) NIPS
    • Li, C.1    Zhu, J.2    Shi, T.3    Zhang, B.4
  • 21
    • 77956544585 scopus 로고    scopus 로고
    • Learning temporal causal graphs for relational timeseries analysis
    • Liu, Y., Niculescu-Mizil, A., Lozano, A. C, and Lu, Y. Learning temporal causal graphs for relational timeseries analysis. In ICML, 2010.
    • (2010) ICML
    • Liu, Y.1    Niculescu-Mizil, A.2    Lozano, A.C.3    Lu, Y.4
  • 22
    • 34948828582 scopus 로고    scopus 로고
    • Unsupervised learning of image transformations
    • Memisevic, R. and Hinton, G. Unsupervised learning of image transformations. In CVPR, 2007.
    • (2007) CVPR
    • Memisevic, R.1    Hinton, G.2
  • 23
    • 84898956512 scopus 로고    scopus 로고
    • Distributed representations of words and phrases and their compositionality
    • Mikolov, T, Sutskever, I., Chen, K., Corrado, G., and Dean, J. Distributed representations of words and phrases and their compositionality. In NIPS, 2013.
    • (2013) NIPS
    • Mikolov, T.1    Sutskever, I.2    Chen, K.3    Corrado, G.4    Dean, J.5
  • 24
    • 84919828137 scopus 로고    scopus 로고
    • Structured recurrent temporal restricted boltzmann machines
    • Mittelman, R., Kuipers, B., Savarese, S., and Lee, H. Structured recurrent temporal restricted boltzmann machines. In ICML, 2014.
    • (2014) ICML
    • Mittelman, R.1    Kuipers, B.2    Savarese, S.3    Lee, H.4
  • 25
    • 84919786239 scopus 로고    scopus 로고
    • Neural variational inference and learning in belief networks
    • Mnih, A. and Gregor, K. Neural variational inference and learning in belief networks. In ICML, 2014.
    • (2014) ICML
    • Mnih, A.1    Gregor, K.2
  • 26
    • 44049116681 scopus 로고
    • Connectionist learning of belief networks
    • Neal, R. M. Connectionist learning of belief networks. In Artificial intelligence, 1992.
    • (1992) Artificial Intelligence
    • Neal, R.M.1
  • 27
    • 84919796093 scopus 로고    scopus 로고
    • Stochastic backpropagation and approximate inference in deep generative models
    • Rezende, D. J., Mohamed, S., and Wierstra, D. Stochastic backpropagation and approximate inference in deep generative models. In ICML, 2014.
    • (2014) ICML
    • Rezende, D.J.1    Mohamed, S.2    Wierstra, D.3
  • 29
    • 84969544782 scopus 로고    scopus 로고
    • Unsupervised learning of video representations using lstms
    • Srivastava, N., Mansimov, E., and Salakhutdinov, R. Unsupervised learning of video representations using lstms. In ICML, 2015.
    • (2015) ICML
    • Srivastava, N.1    Mansimov, E.2    Salakhutdinov, R.3
  • 30
    • 34547997421 scopus 로고    scopus 로고
    • Learning multilevel distributed representations for high-dimensional sequences
    • Sutskever, I. and Hinton, G. E. Learning multilevel distributed representations for high-dimensional sequences. In AISTATS, 2007.
    • (2007) AISTATS
    • Sutskever, I.1    Hinton, G.E.2
  • 31
    • 84858768256 scopus 로고    scopus 로고
    • The recurrent temporal restricted boltzmann machine
    • Sutskever, I., Hinton, G., and Taylor, G. The recurrent temporal restricted boltzmann machine. In NIPS, 2009.
    • (2009) NIPS
    • Sutskever, I.1    Hinton, G.2    Taylor, G.3
  • 32
    • 80053459857 scopus 로고    scopus 로고
    • Generating text with recurrent neural networks
    • Sutskever, I., Martens, J., and Hinton, G. E. Generating text with recurrent neural networks. In ICML, 2011.
    • (2011) ICML
    • Sutskever, I.1    Martens, J.2    Hinton, G.E.3
  • 33
    • 85157999846 scopus 로고    scopus 로고
    • Modeling human motion using binary latent variables
    • Taylor, G., Hinton, G., and Roweis, S. Modeling human motion using binary latent variables. In NIPS, 2006.
    • (2006) NIPS
    • Taylor, G.1    Hinton, G.2    Roweis, S.3
  • 34
    • 71149118574 scopus 로고    scopus 로고
    • Factored conditional restricted boltzmann machines for modeling motion style
    • Taylor, G. W. and Hinton, G. E. Factored conditional restricted boltzmann machines for modeling motion style. In ICML, 2009.
    • (2009) ICML
    • Taylor, G.W.1    Hinton, G.E.2
  • 35


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.