29 October 2018 10:25 AM
The talk is a part of series of talks by highly accomplished Machine Learning Researchers from University of Montreal, Google and Google Brain for the course Deep Learning and Related Methods for Large Dataset Information Processing taught by Michal Fabinger at University of Tokyo.
In his most recent work World Models David Ha demonstrates the unsupervised training of a generative RNN to model RL environments through compressed spatial and temporal representations, achieving state of the art results in various environments. His previous works includes Sketch-RNN , a RNN that constructs stroke-based drawings of common objects. It is trained on thousands of human-drawn images representing hundreds of classes, like cats and crabs and the Mona Lisa. Try it out live and have fun!
David is a Research Scientist at Google Brain. His research interests include Recurrent Neural Networks, Creative AI, and Evolutionary Computing. Prior to joining Google, He worked at Goldman Sachs as a Managing Director, where he co-ran the fixed-income trading business in Japan. He obtained undergraduate and graduate degrees in Engineering Science and Applied Math from the University of Toronto. He was also part of the prestigious Google Brain Residency in 2016 and leads the Google Brain team in Tokyo.