Staff Research Scientist
Beyond Supervised Deep Learning
Halls department, Hall 3
Wednesday, 28 December 2016
15:00 - 16:00
Deep learning has transformed the way in which we design machine learning systems. In this talk, I will provide a brief overview of modern advances to the deep learning paradigm. Starting off with deep reinforcement learning: DQN (Nature, 2015) and A3C (CoRR, 2015), I will then motivate the role of generative modelling in the emerging research landscape and discuss several recent models, including Pixel CNNs (ICML, 2016), AIR (NIPS, 2016) and Conditional 2D->3D (NIPS, 2016).
S. M. Ali Eslami is a staff research scientist at DeepMind. His research is focused on getting computers to learn generative models of images that not only produce good samples but also good explanations for their observations. Prior to this, he was a post-doctoral researcher at Microsoft Research in Cambridge. He did his PhD in the School of Informatics at the University of Edinburgh, during which he was also a visiting researcher in the Visual Geometry Group at the University of Oxford.