MSHL learns higher-order group relations from incomplete spatiotemporal observations via adaptive multi-scale hypergraph Laplacians and a safe neural refinement stage that improves imputation when structure is present.
On a problem of adaptive estimation in gaussian white noise.Theory of Probability & Its Applications, 35(3):454–466, 1991
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Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement
MSHL learns higher-order group relations from incomplete spatiotemporal observations via adaptive multi-scale hypergraph Laplacians and a safe neural refinement stage that improves imputation when structure is present.