Abstract: Equivariant quantum graph neural networks (EQGNNs) offer a potentially powerful method to process graph data. However, existing EQGNN models only consider the permutation symmetry of graphs, ...
Abstract: This paper proposes a method for predicting river water level distribution using time-delay dynamic mode decomposition (tdDMD) and sparse modeling with graph filter banks (GFB). As a ...
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