Despite sanctions, Chinese companies are forging ahead with AI. The Huawei AI stack is optimised to run on the CloudMatrix ...

Graph convolutional neural networks superimpose multi-layer graph convolution operations, which would occur in smoothing phenomena, resulting in performance decreasing as the increasing number of ...

For the field of power operation and inspection, the defect text classification algorithm based on graph convolutional neural network is proposed. And the practical tests in a large defect text ...

To develop a high-performance deep learning workflow, we use HydraGNN [4], an open source distributed implementation [5] of multi-headed graph convolutional neural networks, along with ADIOS, a ...

F. Gama, A. G. Marques, G. Leus, and A. Ribeiro, "Convolutional Neural Network Architectures for Signals Supported on Graphs," IEEE Trans. Signal Process., vol. 67 ...

Central to GSP is the notion of graph convolutional filters which can be used to define convolutional graph neural networks (GNNs). In this paper, we show that the graph convolution can be interpreted ...

Abstract: Graph Convolutional Neural Networks (graph CNNs ... the graph construction and also to facilitate the graph convolution operation for unknown label estimation. Experimental results on seven ...