Introduces tangential Bayes denoiser for Riemannian Gaussian mixtures on manifolds via spectral Laplace-Beltrami approximation, with nearly Bayes risk in low noise and minimax optimality on the circle.
URL https://www.sciencedirect.com/scienc e/chapter/edited-volume/abs/pii/B9780 128147252000108
2 Pith papers cite this work. Polarity classification is still indexing.
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B-NRDEs recast NRDE log-ODE steps via Grossman-Larson and Munthe-Kaas-Wright rooted trees to enable intrinsic Itô and manifold dynamics with a branched signature-kernel objective.
citing papers explorer
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Nonparametric Riemannian Empirical Bayes, and Denoising Measurements on Manifolds
Introduces tangential Bayes denoiser for Riemannian Gaussian mixtures on manifolds via spectral Laplace-Beltrami approximation, with nearly Bayes risk in low noise and minimax optimality on the circle.
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Learning Manifold and It\^o Dynamics with Branched Neural Rough Differential Equations
B-NRDEs recast NRDE log-ODE steps via Grossman-Larson and Munthe-Kaas-Wright rooted trees to enable intrinsic Itô and manifold dynamics with a branched signature-kernel objective.