A Survey on Bayesian Deep Learning

PhD Qualifying Examination


Title: "A Survey on Bayesian Deep Learning"

by

Mr. Hao WANG


Abstract:

While perception tasks such as visual object recognition and text understanding 
play an important role in human intelligence, the subsequent tasks that involve 
inference, reasoning and planning require an even higher level of intelligence. 
The past few years have seen major advances in many perception tasks using deep 
learning models. For higher-level inference, however, probabilistic graphical 
models with their Bayesian nature are still more powerful and flexible. To 
achieve integrated intelligence that involves both perception and inference, it 
is naturally desirable to tightly integrate deep learning and Bayesian models 
within a principled probabilistic framework, which we call Bayesian deep 
learning. In this unified framework, the perception of text or images using 
deep learning can boost the performance of higher-level inference and in 
return, the feedback from the inference process is able to enhance the 
perception of text or images. This survey provides a general introduction to 
Bayesian deep learning and reviews its recent applications on recommender 
systems, topic models, and control. In this survey, we also discuss the 
relationship and differences between Bayesian deep learning and other related 
topics like Bayesian treatment of neural networks.


Date:			Tuesday, 23 February 2016

Time:                  	3:00pm - 5:00pm

Venue:                  Room 1504
                         Lifts 25/26

Committee Members:	Prof. Dit-Yan Yeung (Supervisor)
 			Prof. James Kwok (Chairperson)
 			Dr. Raymond Wong
 			Prof. Nevin Zhang


**** ALL are Welcome ****