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