13 References

References

[Bengio07]
  1. Bengio, P. Lamblin, D. Popovici and H. Larochelle, Greedy Layer-Wise Training of Deep Networks, in Advances in Neural Information Processing Systems 19 (NIPS‘06), pages 153-160, MIT Press 2007.
[Bengio09]
  1. Bengio, Learning deep architectures for AI, Foundations and Trends in Machine Learning 1(2) pages 1-127.
[BengioDelalleau09]
  1. 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, 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, 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]
  1. Lee, C. Ekanadham, and A.Y. Ng., Sparse deep belief net model for visual area V2, in Advances in Neural Information Processing Systems (NIPS) 20, 2008.
[Lee09]
  1. Lee, R. Grosse, R. Ranganath, and A.Y. Ng, “Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.”, ICML 2009
[Ranzato10]
  1. 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, 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]
  1. Vincent, H. Larochelle Y. Bengio and P.A. Manzagol, Extracting and Composing Robust Features with Denoising Autoencoders, Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML‘08), pages 1096 - 1103, ACM, 2008.
[Tieleman08]
  1. Tieleman, Training restricted boltzmann machines using approximations to the likelihood gradient, ICML 2008.
[Xavier10]
  1. Bengio, X. Glorot, Understanding the difficulty of training deep feedforward neuralnetworks, AISTATS 2010
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