Ali Eslami is a senior staff research scientist at Google DeepMind working on problems related to artificial intelligence. Prior to that, he was a post-doctoral researcher at Microsoft Research in Cambridge. He did his Ph.D. 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. His research is focused on figuring out how we can get computers to learn with less human supervision.

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Talk Title:: Neural Priors, Neural Encoders, and Neural Renderers

Abstract:

Scene representation—the process of converting visual sensory data into useful descriptions—is a requirement for intelligent behaviour. Scene representation can be achieved with three components: a prior (which scenes are likely?), an encoder (which scenes correspond to this image?), and a renderer (which images correspond to this scene?). This talk will describe how neural priors, encoders, and renderers can be trained without any human-provided labels, and show how this unlocks new capabilities e.g. in protein structure understanding.