1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces
Samuel I. Berchuck, Mark Janko, Felipe A. Medeiros +2
We introduce a Bayesian non-parametric spatial factor analysis model with spatial dependency induced through a prior on factor loadings. For each column of the loadings matrix, spa…
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma
Samuel I. Berchuck, Felipe A. Medeiros, Sayan Mukherjee
As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well. While methods have been developed to account for high-dimensional…
A spatially varying change points model for monitoring glaucoma progression using visual field data
Samuel I. Berchuck, Jean-Claude Mwanza, Joshua L. Warren
Glaucoma disease progression, as measured by visual field (VF) data, is often defined by periods of relative stability followed by an abrupt decrease in visual ability at some poin…
Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method
Samuel I. Berchuck, Jean-Claude Mwanza, Joshua L. Warren
Diagnosing glaucoma progression is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression uses a longitudinal series of visual fields (V…