Unsupervised models of images by spike-and-slab RBMs

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Volumn , Issue , 2011, Pages 1145-1152

  Courville, Aaron ^a   Bergstra, James ^a   Bengio, Yoshua ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL DISTRIBUTION; HIDDEN LAYERS; NATURAL IMAGES; OBSERVED DATA; RESTRICTED BOLTZMANN MACHINE; STATISTICAL STRUCTURES;

LEARNING SYSTEMS;

REAL VARIABLES;

EID: 80053436198     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Coates, A., Lee, H., and Ng, A. Y. An analysis of single-layer networks in unsupervised feature learning. In AISTATS'2011, 2011.
2. Courville, A., Bergstra, J., and Bengio, Y. The spike and slab restricted Boltzmann machine. In AISTATS'2011, 2011.
3. Hinton, G. E. Training products of experts by minimizing contrastive divergence. Neural Computation, 14:1771-1800, 2002.
4. Hyvärinen, A. and Oja, E. Independent component analysis: Algorithms and applications. Neural Networks, 13 (4-5):411-430, 2000. (Pubitemid 30447427)
5. Krizhevsky, A. Learning multiple layers of features from tiny images. Technical report, University of Toronto, 2009.
6. Krizhevsky, A. Convolutional deep belief networks on CIFAR-10. Technical report, University of Toronto, 2010.
7. Mitchell, T. J. and Beauchamp, J. J. Bayesian variable selection in linear regression. J. Amer. Statistical Assoc., 83(404):1023-1032, 1988.
8. Ranzato, M. and Hinton, G. E. Modeling pixel means and covariance using factorized third-order Boltzmann machines. In CVPR'10. IEEE Press, 2010.
9. Ranzato, M., Mnih, V., and Hinton, G. Generating more realistic images using gated MRF's. In NIPS'10, pp. 2002-2010. MIT Press, 2010.
10. Salakhutdinov, R. and Hinton, G. E. Deep Boltzmann machines. In AISTATS'2009, pp. 448-455, 2009.
11. Tieleman, T. Training restricted Boltzmann machines using approximations to the likelihood gradient. In ICML 2008, pp. 1064-1071, 2008.
12. Williams, C. K. I. and Agakov, F. V. Products of Gaussians and Probabilistic Minor Component Analysis. Neural Computation, 14(5):1169-1182, 2002.