research
∙
02/16/2023
Temporal Graph Neural Networks for Irregular Data
This paper proposes a temporal graph neural network model for forecastin...
share
research
∙
06/10/2022
Scalable Deep Gaussian Markov Random Fields for General Graphs
Machine learning methods on graphs have proven useful in many applicatio...
research
∙
02/18/2020
Deep Gaussian Markov random fields
Gaussian Markov random fields (GMRFs) are probabilistic graphical models...
research
∙
10/18/2019
Anatomically informed Bayesian spatial priors for fMRI analysis
Existing Bayesian spatial priors for functional magnetic resonance imagi...
research
∙
06/25/2019
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analys...
research
∙
03/25/2019