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[R] Graph Neural Ordinary Differential Equations

Paper: https://arxiv.org/abs/1911.07532

Github, blog

Abstract: We extend the framework of graph neural networks (GNN) to continuous time. Graph neural ordinary differential equations (GDEs) are introduced as the counterpart to GNNs where the input–output relationship is determined by a continuum of GNN layers. The GDE framework is shown to be compatible with the majority of commonly used GNN models with minimal modification to the original formulations. We evaluate the effectiveness of GDEs on both static as well as dynamic datasets: results prove their general effectiveness even in cases where the data is not generated by continuous time processes.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.