Sheaf-ADMM trains multi-agent systems by unrolling ADMM with sheaf-specified constraints, yielding improved MNIST robustness to shifts and higher Sudoku solve rates than MPNN baselines.
Learning sheaf laplacians from smooth signals
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HilbNets define convolutions via Hilbert bundle connection Laplacians, prove that sampled Hilbert cellular sheaf Laplacians converge to the continuous operator, and show that discretized networks are consistent and transferable across samplings.
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Learning Multi-Agent Coordination via Sheaf-ADMM
Sheaf-ADMM trains multi-agent systems by unrolling ADMM with sheaf-specified constraints, yielding improved MNIST robustness to shifts and higher Sudoku solve rates than MPNN baselines.
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Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
HilbNets define convolutions via Hilbert bundle connection Laplacians, prove that sampled Hilbert cellular sheaf Laplacians converge to the continuous operator, and show that discretized networks are consistent and transferable across samplings.