Nicolas Le Roux

Nicolas Le Roux, Mark W. Schmidt, Francis Bach: A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets. NIPS 2012: 2672-2680

Rodolphe Jenatton, Nicolas Le Roux, Antoine Bordes, Guillaume Obozinski: A latent factor model for highly multi-relational data. NIPS 2012: 3176-3184

Nicolas Le Roux, Mark W. Schmidt, Francis Bach: A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets. CoRR abs/1202.6258 (2012)

Nicolas Le Roux, Nicolas Heess, Jamie Shotton, John M. Winn: Learning a Generative Model of Images by Factoring Appearance and Shape. Neural Computation 23(3): 593-650 (2011)

Nicolas Heess, Nicolas Le Roux, John M. Winn: Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. ICANN (2) 2011: 9-16

Y-Lan Boureau, Nicolas Le Roux, Francis Bach, Jean Ponce, Yann LeCun: Ask the locals: Multi-way local pooling for image recognition. ICCV 2011: 2651-2658

Mark W. Schmidt, Nicolas Le Roux, Francis Bach: Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. NIPS 2011: 1458-1466

Nicolas Heess, Nicolas Le Roux, John M. Winn: Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs. CoRR abs/1107.3823 (2011)

Nicolas Le Roux, Francis Bach: Local Component Analysis. CoRR abs/1109.0093 (2011)

Mark W. Schmidt, Nicolas Le Roux, Francis Bach: Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. CoRR abs/1109.2415 (2011)

Nicolas Le Roux, Yoshua Bengio: Deep Belief Networks Are Compact Universal Approximators. Neural Computation 22(8): 2192-2207 (2010)

Nicolas Le Roux, Andrew W. Fitzgibbon: A fast natural Newton method. ICML 2010: 623-630

Nicolas Le Roux, Yoshua Bengio: Representational Power of Restricted Boltzmann Machines and Deep Belief Networks. Neural Computation 20(6): 1631-1649 (2008)

Alin Bostan, Frédéric Chyzak, Nicolas Le Roux: Products of ordinary differential operators by evaluation and interpolation. ISSAC 2008: 23-30

Alin Bostan, Frédéric Chyzak, Nicolas Le Roux: Products of Ordinary Differential Operators by Evaluation and Interpolation. CoRR abs/0804.2181 (2008)

Nicolas Le Roux, Yoshua Bengio: Continuous Neural Networks. Journal of Machine Learning Research - Proceedings Track 2: 404-411 (2007)

Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl: Learning the 2-D Topology of Images. NIPS 2007

Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio: Topmoumoute Online Natural Gradient Algorithm. NIPS 2007

Nicolas Le Roux, Moulay A. Barkatou: Rank reduction of a class of pfaffian systems in two variables. ISSAC 2006: 204-211

Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux: The Curse of Highly Variable Functions for Local Kernel Machines. NIPS 2005

Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte: Convex Neural Networks. NIPS 2005

Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux, Jean-François Paiement, Pascal Vincent, Marie Ouimet: Learning Eigenfunctions Links Spectral Embedding and Kernel PCA. Neural Computation 16(10): 2197-2219 (2004)

Yoshua Bengio, Jean-François Paiement, Pascal Vincent, Olivier Delalleau, Nicolas Le Roux, Marie Ouimet: Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. NIPS 2003