10 citations · 30 across the 11 of their papers we have counts for
5 papers · 1 filter
Hidden Markov Models for sepsis detection in preterm infants
Antoine Honore, Dong Liu, David Forsberg +4
We explore the use of traditional and contemporary hidden Markov models (HMMs) for sequential physiological data analysis and sepsis prediction in preterm infants. We investigate t…
Powering Hidden Markov Model by Neural Network based Generative Models
Dong Liu, Antoine Honoré, Saikat Chatterjee +1
Hidden Markov model (HMM) has been successfully used for sequential data modeling problems. In this work, we propose to power the modeling capacity of HMM by bringing in neural net…
Belief Propagation as Fully Factorized Approximation
Dong Liu, Nima N. Moghadam, Lars K. Rasmussen +2
Belief propagation (BP) can do exact inference in loop-free graphs, but its performance could be poor in graphs with loops, and the understanding of its solution is limited. This w…
Neural Network based Explicit Mixture Models and Expectation-maximization based Learning
Dong Liu, Minh Thành Vu, Saikat Chatterjee +1
We propose two neural network based mixture models in this article. The proposed mixture models are explicit in nature. The explicit models have analytical forms with the advantage…
SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials
Saikat Chatterjee, Alireza M. Javid, Mostafa Sadeghi +4
We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices…