5 citations · 7 across the 5 of their papers we have counts for
5 papers
Improving Event Time Prediction by Learning to Partition the Event Time Space
Jimmy Hickey, Ricardo Henao, Daniel Wojdyla +2
Recently developed survival analysis methods improve upon existing approaches by predicting the probability of event occurrence in each of a number pre-specified (discrete) time in…
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
Boyao Li, Alexander J. Thomson, Houssam Nassif +2
Deep neural networks (DNNs) lack the precise semantics and definitive probabilistic interpretation of probabilistic graphical models (PGMs). In this paper, we propose an innovative…
Neural Conditional Event Time Models
Matthew Engelhard, Samuel Berchuck, Joshua D'Arcy +1
Event time models predict occurrence times of an event of interest based on known features. Recent work has demonstrated that neural networks achieve state-of-the-art event time pr…
A Generic Algorithm for Sleep-Wake Cycle Detection using Unlabeled Actigraphy Data
Shanshan Chen, Robert Perera, Matthew M. Engelhard +3
One key component when analyzing actigraphy data for sleep studies is sleep-wake cycle detection. Most detection algorithms rely on accurate sleep diary labels to generate supervis…
Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model
Matthew Engelhard, Hongteng Xu, Lawrence Carin +3
Health risks from cigarette smoking -- the leading cause of preventable death in the United States -- can be substantially reduced by quitting. Although most smokers are motivated…