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List of publications

Titre  
 
[1] Yoshua Bengio, Nicolas Chapados, Olivier Delalleau, Hugo Larochelle, X. Saint-Mleux, Christian Hudon and Jrme Louradour,
"Detonation classification from acoustic signature with the restricted Boltzmann machine",
Computational Intelligence, pp. 261-288, 02 2012.
[2] Hugo Larochelle, Michael Mandel, Razvan Pascanu and Yoshua Bengio,
"Learning algorithms for the classification restricted Boltzmann machine",
Journal of Machine Learning Research, pp. 643-669, 03 2012.
[3] Hugo Larochelle, Yoshua Bengio and Joseph Turian,
"Tractable multivariate binary density estimation and the restricted Boltzmann forest",
Neural Computation, pp. 2285-2307, 09 2010.
[4] Pascal Vincent, Hugo Larochelle, I. Lajoie, Yoshua Bengio and Pierre-Antoine Manzagol,
"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion",
Journal of Machine Learning Research, pp. 3371-3408, 12 2010.
[5] Hugo Larochelle, Yoshua Bengio, J. Louradour and Pascal Lamblin,
"Exploring strategies for training deep neural networks",
Journal of Machine Learning Research, pp. 1-40, 01 2009.
[6] Hugo Larochelle, Yoshua Bengio, J. Louradour and Pascal Lamblin,
"Exploring strategies for training deep neural networks",
Journal of Machine Learning Research, pp. 1-40, 01 2009.
[7] Hugo Larochelle, Dumitru Erhan and Yoshua Bengio,
"Zero-data learning of new tasks",
in Twenty-Third AAAI Conference on Artificial Intelligence, Chicago, Illinois, 07 2008.
[8] Hugo Larochelle and Yoshua Bengio,
"Classification using discriminative restricted Boltzmann machines",
in 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland, pp. 536-543, 07 2008.
[9] Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol,
"Extracting and composing robust features with denoising autoencoders",
in 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland, 07 2008.
[10] Yoshua Bengio, Pascal Lamblin, D. Popovici and Hugo Larochelle,
"Greedy layer-wise training of deep networks",
in Twenty-first Annual Conference on Neural Information Processing Systems, NIPS 21, Vancouver, B.C., 12 2007.
   
     
   
   

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