Hugo La Rochelle Computer scientist
Hugo La Rochelle’s research focuses on machine learning, i.e. in the development of algorithms capable of extracting concepts and abstractions from data. He is particularly interested in deep neural networks, mostly applied in the context of big data and to artificial intelligence problems such as computer vision and natural language processing.
More specifically, his research mainly addresses the following topics:
Tasks: supervised, semi-supervised and unsupervised learning, structured output prediction, ranking, density estimation; Models: deep learning, neural networks, autoencoders, Boltzmann machines, Markov random fields; and Applications: object recognition and tracking, document classification, information retrieval
Google Faculty Research Award, 2013 and 2012.
NSERC Discovery Grant, 2012.
AISTATS Notable Paper Award, 2011.
Y. Bengio et al, "Greedy layer-wise training of deep networks," Adv. Neural Inf. Process. Syst., vol. 19, pp. 153, Jan. 2007.
Associate Director Learning in Machines & Brains
Senior Fellow Learning in Machines & Brains
Google; Université de Montréal
PhD (Computer science) University of Montreal
M.Sc. (Computer science) University of Montreal
BS (Mathematics and computer science) University of Montreal
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