A Kronecker-factorized intrinsic Matérn kernel renders GP-UCB tractable on RIS spaces with up to 10^90 configurations while an online marginal-likelihood adaptive window controller matches hand-tuned performance across speeds without per-deployment calibration.
The GeometricKernels package: Heat and matérn kernels for geometric learning on manifolds, meshes, and graphs
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
other 1
citation-polarity summary
representative citing papers
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.
GABI learns geometry-conditioned latent priors from multi-geometry physical response datasets for use in Bayesian inversion, yielding geometry-adapted posteriors via ABC sampling.
citing papers explorer
-
Geometry-Aware Multi-Armed Bandits for Antenna Beam Selection on Spheres, Tori, $\SO(3)$, and Reconfigurable Intelligent Surfaces
A Kronecker-factorized intrinsic Matérn kernel renders GP-UCB tractable on RIS spaces with up to 10^90 configurations while an online marginal-likelihood adaptive window controller matches hand-tuned performance across speeds without per-deployment calibration.
-
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.
-
Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later
GABI learns geometry-conditioned latent priors from multi-geometry physical response datasets for use in Bayesian inversion, yielding geometry-adapted posteriors via ABC sampling.