Motor imagery (MI) is the mental process of imagining a specific limb movement, such as raising a hand or walking, without physically performing it. These imagined movements generate distinct patterns ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Abstract: A dynamic graph (DG) is commonly encountered in many big data-related application scenarios, like cryptocurrency transaction analysis. A dynamic graph convolutional network (GCN) can ...
Abstract: Graph Convolutional Networks (GCNs) have shown promising results in semi-supervised learning tasks, yet their effectiveness is highly dependent on the quality of the input graph. In image ...
BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...
ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...
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