Portrait de Nicolas Le Roux

Nicolas Le Roux

Membre industriel principal
Chaire en IA Canada-CIFAR
Professeur associé, McGill University, École d'informatique
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Chercheur scientifique, Microsoft Research
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Modèles génératifs
Optimisation

Biographie

Je suis un chercheur universitaire spécialisé dans l'apprentissage automatique, la vision par ordinateur, les réseaux de neurones, l'apprentissage en profondeur, l'optimisation, l'apprentissage à grande échelle et la modélisation statistique en général.

Étudiants actuels

Reyhane Askari Hemmat
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Ioannis Mitliagkas
reyhane.askari.hemmat@mila.quebec
Arnaud Bergeron
Maîtrise recherche - UdeM
Alan Chan
Doctorat - UdeM
Co-superviseur⋅e :
David Scott Krueger
Site web
Google Scholar
Jacob Chmura
Maîtrise recherche - McGill
Co-superviseur⋅e :
Reihaneh Rabbany
Site web
Github
Ekaterina Lobacheva
Postdoctorat - UdeM
Co-superviseur⋅e :
Irina Rish
Site web
Github
Google Scholar

Publications

Negative eigenvalues of the Hessian in deep neural networks
Guillaume Alain
Nicolas Le Roux
Pierre-Antoine Manzagol
The loss function of deep networks is known to be non-convex but the precise nature of this nonconvexity is still an active area of research… (voir plus). In this work, we study the loss landscape of deep networks through the eigendecompositions of their Hessian matrix. In particular, we examine how important the negative eigenvalues are and the benefits one can observe in handling them appropriately.
2018-01-01
ICLR (Workshop) (publié)
openreview.net
openreview.net
BOUNDS LEAD TO IMPROVED CLASSIFIERS
Nicolas Le Roux
The standard approach to supervised classification involves the minimization of a log-loss as an upper bound to the classification error. Wh… (voir plus)ile this is a tight bound early on in the optimization, it overemphasizes the influence of incorrectly classified examples far from the decision boundary. Updating the upper bound during the optimization leads to improved classification rates while transforming the learning into a sequence of minimization problems. In addition, in the context where the classifier is part of a larger system, this modification makes it possible to link the performance of the classifier to that of the whole system, allowing the seamless introduction of external constraints.
2017-01-01
(publié)
www.semanticscholar.org