AI RESEARCH
DeepRV: Accelerating Spatiotemporal Inference with Pre-trained Neural Priors
arXiv CS.LG
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ArXi:2503.21473v3 Announce Type: replace-cross Gaussian Processes (GPs) provide a flexible and statistically principled foundation for modelling spatiotemporal phenomena, but their $O(N^3)$ scaling makes them intractable for large datasets. Approximate methods such as variational inference (VI), inducing-point (sparse) GPs, low-rank kernel approximations (e.g., Nystrom methods and random Fourier features), and approximations such as INLA improve scalability but typically trade off accuracy, calibration, or modelling flexibility. We.