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This work proposes Transferable Graph Metanetworks, which extends the graph metanetwork paradigm with a set of modifications that make performance transferable across input networks of different widths, and proves size-generalization guarantees for the model on $\mu$P-trained inputs.
Any-dimensional machine learning models, such as graph neural networks (GNNs), can be naturally trained and evaluated on inputs of different sizes and dimensions. Inspired by the GNN transferability literature, we show mathematical conditions under which a partial differential equation (PDE) learning-based solver can b...
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