Olshausen b a and field d j 1996 emergence of simple

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Olshausen, B. A. and Field, D. J. (1996). Emergence of simple- cell receptive field properties by learning a sparse code for natural images. Nature , 381 , 607–609. Orr, G. and Muller, K.-R., editors (1998). Neural networks: tricks of the trade . Lect. Notes Comp. Sc. Springer-Verlag. Pascanu, R. and Bengio, Y. (2013). Natural gradient revisited. Technical report, arXiv:1301.3584. Raiko, T., Valpola, H., and LeCun, Y. (2012). Deep learning made easier by linear transformations in perceptrons. In AISTATS’2012 . Raina, R., Battle, A., Lee, H., Packer, B., and Ng, A. Y. (2007). Self-taught learning: transfer learning from unlabeled data. In ICML’2007 . Ranzato, M. and Hinton, G. H. (2010). Modeling pixel means and covariances using factorized third-order Boltzmann machines. In CVPR’2010 , pages 2551–2558. Ranzato, M., Poultney, C., Chopra, S., and LeCun, Y. (2007). Efficient learning of sparse representations with an energy-based model. In NIPS’2006 . Ranzato, M., Boureau, Y., and LeCun, Y. (2008). Sparse feature learning for deep belief networks. In NIPS’2007 . Ranzato, M., Krizhevsky, A., and Hinton, G. (2010a). Factored 3- way restricted Boltzmann machines for modeling natural images. In AISTATS’2010 , pages 621–628. Ranzato, M., Mnih, V., and Hinton, G. (2010b). Generating more realistic images using gated MRF’s. In NIPS’2010 . Ranzato, M., Susskind, J., Mnih, V., and Hinton, G. (2011). On deep generative models with applications to recognition. In CVPR’2011 . Riesenhuber, M. and Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience . Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y. (2011a). Contractive auto-encoders: Explicit invariance during feature ex- traction. In ICML’2011 . Rifai, S., Mesnil, G., Vincent, P., Muller, X., Bengio, Y., Dauphin, Y., and Glorot, X. (2011b). Higher order contractive auto-encoder. In ECML PKDD . Rifai, S., Dauphin, Y., Vincent, P., Bengio, Y., and Muller, X. (2011c). The manifold tangent classifier. In NIPS’2011 . Rifai, S., Bengio, Y., Dauphin, Y., and Vincent, P. (2012). A generative process for sampling contractive auto-encoders. In ICML’2012 . Roweis, S. (1997). EM algorithms for PCA and sensible PCA. CNS Technical Report CNS-TR-97-02, Caltech. Roweis, S. and Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science , 290 (5500). Salakhutdinov, R. (2010a). Learning deep Boltzmann machines using adaptive MCMC. In ICML’2010 . Salakhutdinov, R. (2010b). Learning in Markov random fields using tempered transitions. In NIPS’2010 . Salakhutdinov, R. and Hinton, G. E. (2007). Semantic hashing. In SIGIR’2007 . Salakhutdinov, R. and Hinton, G. E. (2009). Deep Boltzmann machines. In AISTATS’2009 , pages 448–455. Salakhutdinov, R. and Larochelle, H. (2010). Efficient learning of deep Boltzmann machines. In AISTATS’2010 . Salakhutdinov, R., Mnih, A., and Hinton, G. E. (2007). Restricted Boltzmann machines for collaborative filtering. In ICML 2007 . Savard, F. (2011). R´eseaux de neurones `a relaxation entraˆ ın´es par crit`ere d’autoencodeur d´ebruitant . Master’s thesis, U. Montr´eal. Schmah, T., Hinton, G. E., Zemel, R., Small, S. L., and Strother, S. (2009). Generative versus discriminative training of RBMs for classification of fMRI images. In NIPS’2008 , pages 1409–1416.
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