ECE Seminar Series on Modern Artificial Intelligence presents:
GANs AND UNSUPERVISED REPRESENTATION LEARNING
Yoshua Bengio - 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 program on Learning in Machines and Brains, Canada Research Chair in Statistical Learning Algorithms.
One of the central questions for deep learning is how a learning agent could discover good representations in an unsupervised way. First, we consider the still open question of what constitutes a good representation, with the notion of disentangling the underlying factors of variation. Second, we discuss issues with the maximum likelihood framework which has been behind our early work on Boltzmann machines as well as our work on auto-regressive and recurrent neural networks as generative models. These issues motivated our initial development of Generative Adversarial Networks, a research area which has greatly expanded recently. We summarize recent GAN work aiming at better dealing with discrete data, as well as work aiming at generalizing GAN ideas to generative models which are iterative, like Boltzmann machines and denoising auto-encoders. We discuss how adversarial training can be used to obtain invariances to some factors in the representation, and a way to make training with such an adversarial objective more stable by pushing the discriminator score towards the classification boundary but not past it. Finally, we discuss applications of GAN ideas to estimate, minimize or maximize mutual information, entropy or independence between random variables.
Yoshua Bengio Biography
Yoshua Bengio (computer science, 1991, McGill U; post-docs at MIT and Bell Labs, computer science professor at U. Montréal since 1993): he authored three books, over 300 publications (h-index over 100, over 100,000 citations), mostly in deep learning, holds a Canada Research Chair in Statistical Learning Algorithms, is Officer of the Order of Canada, recipient of the Marie-Victorin Quebec Prize 2017, he is a CIFAR Senior Fellow and co-directs its Learning in Machines and Brains program. He is scientific director of the Montreal Institute for Learning Algorithms (MILA), currently the largest academic research group on deep learning. He is on the NIPS foundation board (previously program chair and general chair) and co-created the ICLR conference (specialized in deep learning). He pioneered deep learning and his goal is to uncover the principles giving rise to intelligence through learning, as well as contribute to the development of AI for the benefit of all.
Free and open to the public
This event will be live-streamed on engineering.nyu.edu/live
- April 5, 2018: Stefano Soatto, UCLA and AWS
- May 4, 2018: Vladimir Vapnik, Columbia University and Facebook AI Research