Brown Statistics, NESS Seminar and Charles K. Colver Lectureship Series
Deep learning has caused revolutions in computer perception and natural language understanding. But almost all these successes largely rely on supervised learning, where the machine is required to predict human provided annotations. For game AI, most
systems use model free reinforcement learning, which requires too many trials to be practical in the real world. But animals and humans seem to learn vast amounts of knowledge about how the world works through mere observation and occasional actions. Good predictive world models are an essential component of intelligent
behavior: With them, one can predict outcomes and plan courses of actions. One could argue that good predictive models are the basis of "common sense", allowing us to fill in missing information: predict the future from the past and present, the past from the present, or the state of the world from noisy percepts.
Here, Yann LeCun, PhD, Director of Facebook AI Research and Silver Professor at New York University, will review some principles and methods for predictive learning, and discuss how they can learn hierarchical representations of the world and deal with uncertainty.
Monday, November 27th, 2017
Brown University
