Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2021; 30th International C Igor Farkaš,Paolo Masulli,Stefan Wermter Conference proc

中世纪

An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networksble interest in determining the expressive power mainly of graph neural networks and of graph kernels, to a lesser extent. Most studies have focused on the ability of these approaches to distinguish non-isomorphic graphs or to identify specific graph properties. However, there is often a need for al

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Multi-resolution Graph Neural Networks for PDE Approximatione solution of complex physical problems, in particular relying on Graph Neural Networks applied on a mesh of the domain at hand. On the other hand, state-of-the-art deep approaches of image processing use different resolutions to better handle the different scales of the images, thanks to pooling an

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https://doi.org/10.1007/978-3-662-53310-9d on the famed U-Net. These approaches are experimentally validated on a diffusion problem, compared with projected CNN approach and the experiments witness their efficiency, as well as their generalization capabilities.

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https://doi.org/10.1007/978-3-662-53310-9the tail entities. Based on that, each relation is a rotation from the head entities to the tail entities on the hyperplane in complex vector space. Experiments on well-known datasets show the improvement of the proposed model compared to other models.

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Grundlagen zum Schneideneingriff,ple Feed-forward based Interaction Model (FIM) and a Convolutional network based Interaction Model (CIM). Through extensive experiments conducted on three benchmark datasets, we demonstrate the advantages of our interaction mechanism, both of them achieving state-of-the-art performance consistently.

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