Xi-Zhao Wang worked as a director and a professor in Big Data Institute of ShenZhen University since 2013. Prof. Wang’s major research interests include uncertainty modeling and machine learning. He has completed more than 30 research projects, edited 10+ special issues, published 3 monographs, 2 textbooks, and 200+ peer-reviewed research papers, and has supervised more than 200 Mphil and PhD students. Prof. Wang is a CAAI Fellow, an IEEE Fellow, the chair of IEEE SMC Technical Committee on Computational Intelligence, and the Chief Editor of Springer Journal - Machine Learning and Cybernetics. Prof. Wang was the recipient of the IEEE SMCS Outstanding Contribution Award, and was a distinguished lecturer of the IEEE SMCS.
Speech title: Uncertainty Modelling in Deep Learning
Uncertainty modelling is an old topic but its application in deep learning just starts in last decade. This talk briefly introduces the basic frameworks of deep learning and makes a short analysis on the successes and failures of deep learning. It then discusses some fundamental issues of uncertainty modelling regarding data, model, and prediction in deep learning, focusing on the critical impact of uncertainty representation and processing on each phase of the entire learning process. The talk finally reports several results in recent years regarding uncertainty modelling in adversarial learning.