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cs.LG2026
Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling
Pedro Sousa, Will Tebbutt, Sadiq Jaffer +3
Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near…
cs.LG2021
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Will Tebbutt, Arno Solin, Richard E. Turner
Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temporal modelling problems such as those in climate science and epidemiology. Howev…