4 papers · 1 filter
Deep Probabilistic Spatial Modeling for Multivariate Mixed-Type Responses
Yeseul Jeon, Kyeong Eun Lee, Joon Jin Song
Many scientific applications involve mixed spatially indexed outcomes of heterogeneous types that are driven by shared latent mechanisms. Modeling such data is challenging due to c…
Uncertainty-Aware Neural Multivariate Geostatistics
Yeseul Jeon, Aaron Scheffler, Rajarshi Guhaniyogi
We propose Deep Neural Coregionalization, a scalable framework for uncertainty-aware multivariate geostatistics. DNC models multivariate spatial effects through spatially varying l…
Deep Generative Modeling with Spatial and Network Images: An Explainable AI (XAI) Approach
Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler
This article addresses the challenge of modeling the amplitude of spatially indexed low frequency fluctuations (ALFF) in resting state functional MRI as a function of cortical stru…
Interpretable Deep Neural Network for Modeling Functional Surrogates
Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler +2
Developing surrogates for computer models has become increasingly important for addressing complex problems in science and engineering. This article introduces an artificial intell…