Inference in probabilistic models with applications to communications and signal processing

Abstract

This talk shall briefly overview the recent advances of machine learning techniques at the University of Hong Kong, and how they are applied to communications and image processing problems. The specific topics to be introduced include Gaussian belief propagation, variational statistical inference and tensor processing.Surprisingly, many machine learning models are general enough to tackle problems that are seemingly unrelated.The applications to be covered include synchronization in large-scale networks, channel estimation under high mobility, power state estimation in smart grid, massive MIMO channel estimation, face classification, surveillance video objects separation, and image de-noising.