Proves that rescaled deviations of kernel gradient flow and infinitesimal gradient boosting from their deterministic ODE limits converge to a Gaussian process via a general stochastic perturbation analysis of ODEs in Banach spaces.
Journal of spatial information science , 1–17
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ProbGLC unifies probabilistic and deterministic geolocalization models to deliver state-of-the-art accuracy (0.86 Acc@1km) plus uncertainty quantification on multi-disaster cross-view datasets.
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A functional central limit theorem for kernel gradient flow and infinitesimal gradient boosting
Proves that rescaled deviations of kernel gradient flow and infinitesimal gradient boosting from their deterministic ODE limits converge to a Gaussian process via a general stochastic perturbation analysis of ODEs in Banach spaces.