23 citations · 27 across the 5 of their papers we have counts for
5 papers
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…
Lifted Tree-Reweighted Variational Inference
Hung Hai Bui, Tuyen N. Huynh, David Sontag
We analyze variational inference for highly symmetric graphical models such as those arising from first-order probabilistic models. We first show that for these graphical models, t…
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Tran The Truyen, Dinh Q. Phung, Hung H. Bui +1
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov ch…