Discussion of "the neural autoregressive distribution estimator"

Journal of Machine Learning Research

Volumn 15, Issue , 2011, Pages 38-39

  Bengio, Yoshua ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AUTO-REGRESSIVE; GENERALIZATION PERFORMANCE; GRAPHICAL MODEL; LATENT VARIABLE; LOG LIKELIHOOD; MULTINOMIALS; MULTIVARIATE BERNOULLI; RESTRICTED BOLTZMANN MACHINE;

SOFTWARE ENGINEERING;

ARTIFICIAL INTELLIGENCE;

EID: 84862529754     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (7)

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2. Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1-127. Also published as a book. Now Publishers, 2009.
3. Frey, B. J. (1998). Graphical models for machine learning and digital communication. MIT Press.
4. Hinton, G. E., Osindero, S., and Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527-1554.
5. Larochelle, H. and Murray, I. (2011). The Neural Autoregressive Distribution Estimator. In AISTATS'2011 .
6. Larochelle, H., Bengio, Y., and Turian, J. (2010). Tractable multivariate binary density estimation and the restricted Boltzmann forest. Neural Computation, 22(9), 2285-2307.
7. Smolensky, P. (1986). Information processing in dynamical systems: Foundations of harmony theory. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing, volume 1, chapter 6, pages 194-281.MIT Press, Cambridge.