Graph neural networks (GNNs) is an information - processing system that uses message passing among graph nodes. In recent years, GNN variants including graph attention network (GAT), graph ...

This is the official implementation of the paper Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction. This paper proposes a novel message passing ...

A graph network takes a graph as input and returns a graph as output. The input graph has edge- (E), node- (V), and global-level (u) attributes. The output graph has the same structure, but updated ...

"We use modern deep neural network architectures to retrieve the input images from the scrambled output of the fiber," said Demetri Psaltis, Swiss Federal Institute of Technology, Lausanne, who ...

MA message-passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph. However, most of these studies assumed that ...

Graph neural networks (GNNs) is an information - processing system that uses message passing among graph nodes. In recent years, GNN variants including graph attention network (GAT), graph ...