Deep Learning and Unsupervised Feature Learning
Yoshua Bengio · James Bergstra · Quoc V. Le

Sat Dec 8th 07:30 AM -- 06:30 PM @ Emerald Bay B, Harveys Convention Center Floor (CC)
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Machine learning algorithms are very sensitive to the representations chosen for the data so it is desirable to improve learning algorithms that can discover good representations, good features, or good explanatory latent variables. Both supervised and unsupervised learning algorithms have been proposed for this purpose, and they can be combined in semi-supervised setups in order to take advantage of vast quantities of unlabeled data. Deep learning algorithms have multiple levels of representation and the number of levels can be selected based on the available data. Great progress has been made in recent years in algorithms, their analysis, and their application both in academic benchmarks and large-scale industrial settings (such as machine vision/object recognition and NLP, including speech recognition). Many interesting open problems also remain, which should stimulate lively discussions among the participants.

Author Information

Yoshua Bengio (University of Montreal)

Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.

James Bergstra (Kindred)
Quoc V. Le (Google)

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