Title: Bayesian principles for learning machines
Authors: Mohammad Emtiyaz Khan - RIKEN Center for AI project (Japan) [presenting]
Abstract: Humans and animals have a natural ability to learn and quickly adapt to their surroundings autonomously. How can we design machines that do the same? We will present Bayesian principles to bridge such gaps between humans and machines. We will show that a wide variety of machine-learning algorithms are instances of a single learning-rule derived from Bayesian principles. The rule unravels a dual-perspective yielding new mechanism for knowledge transfer in learning machines. It is claimed that Bayesian principles are indispensable for an AI that learns as efficiently as we do.