Search Results for author:
Found 5 papers, 3 papers with code
Temporal Graph Neural Networks for Irregular Data
Our TGNN4I model is designed to handle both irregular time steps and partial observations of the graph.
DINO as a von Mises-Fisher mixture model
With this interpretation, DINO assumes equal precision for all components when the prototypes are also $L^2$-normalized.
Ranked #24 on
Self-Supervised Image Classification
on ImageNet
Self-Supervised Image Classification
Self-Supervised Learning
+1
Scalable Deep Gaussian Markov Random Fields for General Graphs
We propose a flexible GMRF model for general graphs built on the multi-layer structure of Deep GMRFs, originally proposed for lattice graphs only.
Deep Gaussian Markov Random Fields
Gaussian Markov random fields (GMRFs) are probabilistic graphical models widely used in spatial statistics and related fields to model dependencies over spatial structures.
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data.
Methodology Applications Computation
