Publications for topic "Deep architectures" sorted by recency
| 1-25 | 26-31 |

Dumitru Erhan, Aaron Courville, Yoshua Bengio and Pascal Vincent, Why Does Unsupervised Pre-training Help Deep Learning?, in: JMLR W\&CP: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), Chia Laguna Resort, Sardinia, Italy, pages 201-208, 2010
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Xavier Glorot and Yoshua Bengio, Understanding the difficulty of training deep feedforward neural networks, in: JMLR W\&CP: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), Chia Laguna Resort, Sardinia, Italy, pages 249-256, 2010
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Yoshua Bengio, Olivier Delalleau and Clarence Simard, Decision Trees do not Generalize to New Variations (2010), in: Computational Intelligence, 26:4(449--467)
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Joseph Turian, James Bergstra and Yoshua Bengio, Quadratic Features and Deep Architectures for Chunking, in: North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT), pages 245--248, Association for Computational Linguistics, 2009
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Hugo Larochelle, Dumitru Erhan and Pascal Vincent, Deep Learning using Robust Interdependent Codes, in: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009), Clearwater (Florida), USA, pages 312--319, 2009
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Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio and Pascal Vincent, The Difficulty of Training Deep Architectures and the effect of Unsupervised Pre-Training, in: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009), Clearwater (Florida), USA, pages 153--160, 2009
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Yoshua Bengio, Jerome Louradour, Ronan Collobert and Jason Weston, Curriculum Learning, Département d'informatique et recherche opérationnelle, Université de Montréal, number 1330, 2009
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Yoshua Bengio, Learning deep architectures for AI (2009), in: Foundations and Trends in Machine Learning, 2:1(1--127)
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Guillaume Desjardins and Yoshua Bengio, Empirical Evaluation of Convolutional RBMs for Vision, Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, number 1327, 2008
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Yoshua Bengio and Olivier Delalleau, Justifying and Generalizing Contrastive Divergence (2009), in: Neural Computation, 21:6(1601--1621)
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Yoshua Bengio, Hugo Larochelle and Joseph Turian, Deep Woods, Poster presented at the Learning@Snowbird Workshop, Snowbird, USA, 2008, 2008
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Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol, Extracting and Composing Robust Features with Denoising Autoencoders, in: Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08), pages 1096--1103, ACM, 2008
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Hugo Larochelle and Yoshua Bengio, Classification using Discriminative Restricted Boltzmann Machines, in: Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08), Helsinki, Finland, pages 536--543, ACM, 2008
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Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol, Extracting and Composing Robust Features with Denoising Autoencoders, Département d'Informatique et Recherche Opérationnelle, Université de Montréal, number 1316, 2008
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Yoshua Bengio, Learning deep architectures for AI, Dept. IRO, Universite de Montreal, number 1312, 2007
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Yoshua Bengio and Olivier Delalleau, Justifying and Generalizing Contrastive Divergence, Département d'Informatique et Recherche Opérationnelle, Université de Montréal, number 1311, 2007
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