Constant-depth ReLU networks of size O(n²d) exist that deterministically generate graphs within edit distance d from any given n-vertex input graph.
Learning shape correspondence with anisotropic convolutional neural networks
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Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
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ReLU Networks for Exact Generation of Similar Graphs
Constant-depth ReLU networks of size O(n²d) exist that deterministically generate graphs within edit distance d from any given n-vertex input graph.
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.