Abstract:Graph Convolutional Networks (GCNs) have attracted broad attention from industry and academia, for which GCNs have demonstrated powerful ability to model the irregular data, e.g., skeletal data and graph-structured data. The most existing effective model may be the fully hyperbolic graph neural network. However, it involves a large number of parameters, thus consuming considerable computing resources. In this paper, we propose a model based on adaptive frequency filter and corresponding optimizer in hyperbolic… Show more
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