Nicolas Le Roux

I am a postdoc in the Machine Learning and Perception Group at Microsoft Research. I am mainly interested in designing new architectures of deep neural networks and efficient ways to train them.
Previously, I was a PhD student at the LISA laboratory in University of Montreal under the supervision of Yoshua Bengio. My old webpage can be found here.

Here is my Curriculum Vitae, in which you will find my email address.

Publications

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The full list of my publications (including technical reports) is available here.

PhD thesis

• Avancées théoriques sur la représentation et l’optimisation des réseaux de neurones (in French)
  N. Le Roux
  University of Montreal

Peer-reviewed articles

• Representational Power of Restricted Boltzmann Machines and Deep Belief Networks (2008)
  N. Le Roux and Y. Bengio
  Neural Computation, volume 20, p. 1631-1649
• Learning the 2-D Topology of Images (2008)
  N. Le Roux, Y. Bengio, P. Lamblin, M. Joliveau and B. Kegl
  In Advances in Neural Information Processing Systems 20, MIT Press, Cambridge, MA
• Topmoumoute Online Natural Gradient Algorithm (2008)
  N. Le Roux, P.A. Manzagol and Y. Bengio
  In Advances in Neural Information Processing Systems 20, MIT Press, Cambridge, MA
• Continuous Neural Networks (2007)
  N. Le Roux and Y. Bengio
  In Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics
  
• Convex Neural Networks (2006)
  Y. Bengio, N. Le Roux, P. Vincent, O. Delalleau and P. Marcotte
  In Advances in Neural Information Processing Systems 18, MIT Press, Cambridge, MA
• The Curse of Highly Variable Functions for Local Kernel Machines (2006)
  Y. Bengio, O. Delalleau, and N. Le Roux
  In Advances in Neural Information Processing Systems 18, MIT Press, Cambridge, MA
• Efficient Non-Parametric Function Induction in Semi-Supervised Learning (2006)
  O. Delalleau Y. Bengio, and N. Le Roux
  In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics
• Learning Eigenfunctions Links Spectral Embedding and Kernel PCA (2004)
  Y. Bengio, O. Delalleau, N. Le Roux, J.-F. Paiement, M. Ouimet and P. Vincent
  In Neural Computation, volume 16, issue 10
• Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering (2004)
  Y. Bengio, J.-F. Paiement, P. Vincent, O. Delalleau, N. Le Roux and M. Ouimet
  In Advances in Neural Information Processing Systems 16, MIT Press, Cambridge, MA

Book chapters

• Spectral Dimensionality Reduction (2006)
  Y. Bengio, O. Delalleau, N. Le Roux, J.-F. Paiement, P. Vincent and M. Ouimet
  Chapter of the book Feature Extraction, Foundations and Applications by Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lofti Zadeh
• Label propagation and quadratic criterion (2005)
  Y. Bengio, O. Delalleau and N. Le Roux
  Chapter of the book Semi-supervised learning by O. Chapelle, Bernhard Schölkopf and Alexander Zien
• Large-scale algorithms (2005)
  O. Delalleau Y. Bengio, and N. Le Roux
  Chapter of the book Semi-supervised learning by O. Chapelle, Bernhard Schölkopf and Alexander Zien