Search Results for author:
Found 2 papers, 2 papers with code
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain Connectivity
While typically the Graph U-Net is a node-focused architecture where graph embedding depends mainly on node attributes, we propose a graph-focused architecture where the node feature embedding is based on the graph topology.
GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes
Catchy but rigorous deep learning architectures were tailored for image super-resolution (SR), however, these fail to generalize to non-Euclidean data such as brain connectomes.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
