We explore the following crucial question: How could brains potentially perform the kind of powerful credit assignment that allows hidden layers of a very deep network to be trained and that has been so successful with deep learning recently?
27 September 2016, Redwood Center for Theoretical Neuroscience, Berkeley University
Yoshua Bengio is Full Professor of the Department of Computer Science and Operations Research, head of the Montreal Institute for Learning Algorithms (MILA), CIFAR Program co-director of the CIFAR Neural Computation and Adaptive Perception program, Canada Research Chair in Statistical Learning Algorithms. His main research ambition is to understand principles of learning that yield intelligence. He teaches a graduate course in Machine Learning (IFT6266) and supervises a large group of graduate students and post-docs. His research is widely cited (over 40000 citations found by Google Scholar in mid-2016, with an H-index of 84).
Yoshua Bengio is currently action editor for the Journal of Machine Learning Research, associate editor for the Neural Computation journal, editor for Foundations and Trends in Machine Learning, and has been associate editor for the Machine Learning Journal and the IEEE Transactions on Neural Networks.
Yoshua Bengio was Program Chair for NIPS’2008 and General Chair for NIPS’2009 (NIPS is the flagship conference in the areas of learning algorithms and neural computation). Since 1999, he has been co-organizing the Learning Workshop with Yann Le Cun, with whom he has also created the International Conference on Representation Learning (ICLR). He has also organized or co-organized numerous other events, principally the deep learning workshops and symposiua at NIPS and ICML since 2007.
http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html
