Abstract: Graph Convolutional Networks (GCNs) have been widely studied for semi-supervised learning tasks. It is known that the graph convolution operations in most of existing GCNs are composed of ...
The choice of the loss function is critical in extreme multi-label learning where the objective is to annotate each data point with the most relevant {\it subset} of labels from an extremely large ...
Abstract: Graph neural networks (GNNs) have achieved considerable success in dealing with graph-structured data by the message-passing mechanism. Actually, this mechanism relies on a fundamental ...
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