In this paper, we present a Repre- sentative Graph (RepGraph) layer to dynamically sample a few represen- tative features, which dramatically reduces redundancy ...
Aug 12, 2020 · In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically ...
Apr 16, 2022 · This article summarizes a selection of the emerging theoretical results on approximation and learning properties of widely used message passing ...
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Sep 2, 2021 · This article is one of two Distill publications about graph neural networks. Take a look at Understanding Convolutions on Graphs.
Jul 15, 2022 · This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures.
To deepen our understanding of graph neural networks, we investigate the repre- sentation power of Graph Convolutional Networks (GCN) through the looking.
Sep 11, 2023 · Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs.
Nov 9, 2020 · In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry.
Duration: 25:30
Posted: Jan 4, 2024
Posted: Jan 4, 2024