892 citations · 2.3k across the 60 of their papers we have counts for
12 papers · 1 filter
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…
Learning From Ordered Sets and Applications in Collaborative Ranking
Truyen Tran, Dinh Phung, Svetha Venkatesh
Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed stars to them, or a customized tour package. It turns out,…
Learning Structured Outputs from Partial Labels using Forest Ensemble
Truyen Tran, Dinh Phung, Svetha Venkatesh
Learning structured outputs with general structures is computationally challenging, except for tree-structured models. Thus we propose an efficient boosting-based algorithm AdaBoos…