A Lie-algebraic kernel reparameterizes 3D rotationally anisotropic Gaussian processes with explicit principal length-scales and SO(3) orientations, matching full SPD flexibility but improving interpretability over axis-aligned ARD.
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Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.
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Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes
A Lie-algebraic kernel reparameterizes 3D rotationally anisotropic Gaussian processes with explicit principal length-scales and SO(3) orientations, matching full SPD flexibility but improving interpretability over axis-aligned ARD.
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On What We Can Learn from Low-Resolution Data
Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.