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Manifold limit for the training of shallow graph convolutional neural networks
We study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point clouds under a manifold assumption.
Manifold limit for the training of shallow graph convolutional neural networks
TLDR
We study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point clouds under a manifold assumption.