-
Generalization bounds for graph convolutional neural networks via Rademacher ...
This paper aims at studying the sample complexity of graph convolutional neural networks (GCNs), by providing tight upper bounds of Rademacher complexity for GCN models with a... -
GRAPH CONVOLUTIONAL NETWORKS FROM THE PER-SPECTIVE OF SHEAVES AND THE NEURAL ...
Graph convolutional networks are a popular class of deep neural network algorithms which have shown success in a number of relational learning tasks. Despite their success,... -
Distance-Geometric Graph Convolutional Network (DG-GCN) for Three-Dimensional...
Distance-geometric graph representation for 3D graphs, utilizing continuous-filter convolutional layers with filter-generating networks. -
Spatio-Temporal Graph Convolutional Networks
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
