Hierarchical Probabilistic Models and Deep Neural Networks

[The talk is cancelled]

Speaker:        Professor Wray Buntine
                Monash University

Title:          "Hierarchical Probabilistic Models and Deep Neural

Date:           Monday, 17 September 2018

Time:           4:00pm - 5:00pm

Venue:          Lecture Theater F (near lift 25/26), HKUST


In this talk I will first describe some of our recent work with
hierarchical probabilistic models.  These are currently among the state of
the art in classification and in topic modelling:  k-dependence Bayesian
networks and hierarchical topic models, respectively, and both are deep
models in a different sense.  These represent some of the leading edge
machine learning technology prior to the advent of deep neural networks.
Then for deep neural networks, I will describe as a point of comparison
some of the state of the art applications I am familiar with: multi-task
learning, document classification, and learning to learn.  These build on
the RNNs widely used in semi-structured learning.  The old and the new are
remarkably different.  So what are the new capabilities deep neural
networks have yielded?  Do we even need the old technology?  What can we
do next?


Wray Buntine is a full professor at Monash University from 2014 and is
director of the Master of Data Science, the Faculty of IT's newest and
in-demand degree. He was previously at NICTA Canberra, Helsinki Institute
for Information Technology where he ran a semantic search project, NASA
Ames Research Center, University of California, Berkeley, and Google, as
well as several startups. He is known for his theoretical and applied work
and in probabilistic methods for document and text analysis, social
networks, data mining and machine learning.

More details can be found at his website: https://topicmodels.org/about/