32 citations · 111 across the 35 of their papers we have counts for
10 papers · 1 filter
Stabilizing Linear Prediction Models using Autoencoder
Shivapratap Gopakumar, Truyen Tran, Dinh Phung +1
To date, the instability of prognostic predictors in a sparse high dimensional model, which hinders their clinical adoption, has received little attention. Stable prediction is oft…
Outlier Detection on Mixed-Type Data: An Energy-based Approach
Kien Do, Truyen Tran, Dinh Phung +1
Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as contin…
Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data
Truyen Tran, Wei Luo, Dinh Phung +3
Preterm births occur at an alarming rate of 10-15%. Preemies have a higher risk of infant mortality, developmental retardation and long-term disabilities. Predicting preterm birth…
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…