.. _references:

==========

References

==========

.. [Bengio07] Y. Bengio, P. Lamblin, D. Popovici and H. Larochelle, `Greedy Layer-Wise Training of Deep Networks <http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/190>`_, in Advances in Neural Information Processing Systems 19 (NIPS'06), pages 153-160, MIT Press 2007.

.. [Bengio09] Y. Bengio, `Learning deep architectures for AI <http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/239>`_, Foundations and Trends in Machine Learning 1(2) pages 1-127.

.. [BengioDelalleau09] Y. Bengio, O. Delalleau, Justifying and Generalizing Contrastive Divergence (2009), Neural Computation, 21(6): 1601-1621.

.. [BoulangerLewandowski12] N Boulanger-Lewandowski, Y. Bengio and P. Vincent, `Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription <http://www-etud.iro.umontreal.ca/~boulanni/icml2012>`_, in Proceedings of the 29th International Conference on Machine Learning (ICML), 2012.

.. [Fukushima] Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36, 193–202.

.. [Hinton06] G.E. Hinton and R.R. Salakhutdinov, `Reducing the Dimensionality of Data with Neural Networks <http://www.cs.toronto.edu/~rsalakhu/papers/science.pdf>`_, Science, 28 July 2006, Vol. 313. no. 5786, pp. 504 - 507.

.. [Hinton07] G.E. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets", Neural Computation, vol 18, 2006

.. [Hubel68] Hubel, D. and Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology (London), 195, 215–243.

.. [LeCun98] LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998d). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.

.. [Lee08] H. Lee, C. Ekanadham, and A.Y. Ng., `Sparse deep belief net model for visual area V2 <http://www.stanford.edu/~hllee/nips07-sparseDBN.pdf>`_, in Advances in Neural Information Processing Systems (NIPS) 20, 2008.

.. [Lee09] H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng, "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.", ICML 2009

.. [Ranzato10] M. Ranzato, A. Krizhevsky, G. Hinton, "Factored 3-Way Restricted Boltzmann Machines for Modeling Natural Images". Proc. of the 13-th International Conference on Artificial Intelligence and Statistics (AISTATS 2010), Italy, 2010

.. [Ranzato07] M.A. Ranzato, C. Poultney, S. Chopra and Y. LeCun, in J. Platt et al., `Efficient Learning of Sparse Representations with an Energy-Based Model <http://yann.lecun.com/exdb/publis/pdf/ranzato-06.pdf>`_, Advances in Neural Information Processing Systems (NIPS 2006), MIT Press, 2007.

.. [Serre07] Serre, T., Wolf, L., Bileschi, S., and Riesenhuber, M. (2007). Robust object recog- nition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell., 29(3), 411–426. Member-Poggio, Tomaso.

.. [Vincent08] P. Vincent, H. Larochelle Y. Bengio and P.A. Manzagol, `Extracting and Composing Robust Features with Denoising Autoencoders <http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/217>`_, Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08), pages 1096 - 1103, ACM, 2008.

.. [Tieleman08] T. Tieleman, Training restricted boltzmann machines using approximations to the likelihood gradient, ICML 2008.

.. [Xavier10] Y. Bengio, X. Glorot, Understanding the difficulty of training deep feedforward neuralnetworks, AISTATS 2010