100 citations · 778 across the 90 of their papers we have counts for
14 papers · 1 filter
Human Activity Learning and Segmentation using Partially Hidden Discriminative Models
Truyen Tran, Hung Bui, Svetha Venkatesh
Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such…
Boosted Markov Networks for Activity Recognition
Truyen Tran, Hung Bui, Svetha Venkatesh
We explore a framework called boosted Markov networks to combine the learning capacity of boosting and the rich modeling semantics of Markov networks and applying the framework for…
MCMC for Hierarchical Semi-Markov Conditional Random Fields
Truyen Tran, Dinh Phung, Svetha Venkatesh +1
Deep architecture such as hierarchical semi-Markov models is an important class of models for nested sequential data. Current exact inference schemes either cost cubic time in sequ…
Mixed-Variate Restricted Boltzmann Machines
Truyen Tran, Dinh Phung, Svetha Venkatesh
Modern datasets are becoming heterogeneous. To this end, we present in this paper Mixed-Variate Restricted Boltzmann Machines for simultaneously modelling variables of multiple typ…
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities
Truyen Tran, Dinh Phung, Svetha Venkatesh
We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the T…
Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis
Truyen Tran, Dinh Phung, Svetha Venkatesh
Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted…