Abstract: Graph Convolutional Networks (GCNs) prevail in the analysis of network-structured data, but how the graph size affects the performance is not fully understood. As the limit of graphs, the ...
Improved Modeling and Generalization Capabilities of Graph Neural Networks With Legendre Polynomials
Abstract: LegendreNet is a novel graph neural network (GNNs) model that addresses stability issues present in traditional GNN models such as ChebNet, while also more effectively capturing higher-order ...
Uses data from multiple countries and polynomial regression algrothims to predict carbon emmisions in 2050. Also creates a 3D graph to vizualize the last 20 years of Canada's GDP, and Canda's Carbon ...
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] ...
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