Abstract: A crucial ingredient of deep learning is that of learning representations, more specifically with the objective to discover higher-level representations which capture and disentangle explanatory factors. This is a very ambitious goal and current state-of-the-art techniques still fall short, often capturing mostly superficial features of the data, which leaves them vulnerable to adversarial attacks and insufficient out-of-distribution robustness.This talk will review these original objectives, supervised and unsupervised approaches, and outline research ideas towards better representation learning.
Yoshua Bengio is recognized as one of the world’s artificial intelligence leaders and a pioneer of deep learning. Professor since 1993 at the Université de Montréal, he received the A.M. Turing Award 2018 with Geoff Hinton and Yann LeCun, considered like the Nobel prize for computing. Holder of the Canada Research Chair in Statistical Learning Algorithms, he is also the founder and scientific director of Mila, the Quebec Institute of Artificial Intelligence, which is the world’s largest university-based research group in deep learning. In 2018, he collected the largest number of new citations in the world for a computer scientist. He earned the prestigious Killam Prize from the Canada Council for the Arts and the Marie-Victorin Quebec Prize. Concerned about the social impact of AI, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.