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Publications of Nicolas Le Roux
2011
| , and , Improving first and second-order methods by modeling uncertainty, in: Optimization for Machine Learning, MIT Press, 2011 |
2010
| and , Deep Belief Networks are Compact Universal Approximators (2010), in: Neural Computation, 22:8(2192-2207) |
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2008
| , Avancées théoriques sur la représentation et l'optimisation des réseaux de neurones, Université de Montréal, 2008 |
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| , , , and , Learning the 2-D Topology of Images, in: Advances in Neural Information Processing Systems 20 (NIPS'07), pages 841--848, MIT Press, 2008 |
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| and , Representational Power of Restricted Boltzmann Machines and Deep Belief Networks (2008), in: Neural Computation, 20:6(1631--1649) |
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| , and , Topmoumoute online natural gradient algorithm, in: Advances in Neural Information Processing Systems 20 (NIPS'07), MIT Press, 2008 |
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2007
| and , Continuous Neural Networks, in: Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS'07), Omnipress, 2007 |
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| and , Representational Power of Restricted Boltzmann Machines and Deep Belief Networks, Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, number 1294, 2007 |
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| , and , Topmoumoute online natural gradient algorithm, Département d'informatique et recherche opérationnelle, Université de Montréal, number 1299, 2007 |
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2006
| and , Continuous Neural Networks, Département d'informatique et recherche opérationnelle, Université de Montréal, number 1281, 2006 |
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| , , , and , Convex Neural Networks, in: Advances in Neural Information Processing Systems 18 (NIPS'05), pages 123--130, MIT Press, 2006 |
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| , and , Label Propagation and Quadratic Criterion, in: Semi-Supervised Learning, pages 193--216, {MIT} Press, 2006 |
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| , and , Large-Scale Algorithms, in: Semi-Supervised Learning, pages 333--341, {MIT} Press, 2006 |
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| , , , , and , Spectral Dimensionality Reduction, in: Feature Extraction, Foundations and Applications, Springer, 2006 |
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| , and , The Curse of Highly Variable Functions for Local Kernel Machines, in: Advances in Neural Information Processing Systems 18 (NIPS'05), pages 107--114, MIT Press, 2006 |
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2005
| , , , and , Convex neural networks, Département d'informatique et recherche opérationnelle, Université de Montréal, number 1263, 2005 |
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| , and , Efficient Non-Parametric Function Induction in Semi-Supervised Learning, in: Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS'05), Savannah Hotel, Barbados, pages 96--103, Society for Artificial Intelligence and Statistics, 2005 |
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| , and , The Curse of Dimensionality for Local Kernel Machines, Département d'informatique et recherche opérationnelle, Université de Montréal, number 1258, 2005 |
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2004
| , and , Efficient Non-Parametric Function Induction in Semi-Supervised Learning, Département d'informatique et recherche opérationnelle, Université de Montréal, number 1247, 2004 |
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| , , , , and , Learning eigenfunctions links spectral embedding and kernel PCA (2004), in: Neural Computation, 16:10(2197--2219) |
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| , , , , and , Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering, in: Advances in Neural Information Processing Systems 16 (NIPS'03), 2004 |
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2003
| , , , , and , Spectral Clustering and Kernel PCA are Learning Eigenfunctions, Département d'informatique et recherche opérationnelle, Université de Montréal, number 1239, 2003 |
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