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cs.LG2026
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
Yahya Aalaila, Mouad Elhamdi, Gerrit Großmann +3
Spatio-temporal point-process models must often generalise across space when local event histories are sparse. We study whether exogenous spatial context can compensate in such reg…
cs.LG2025
Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes
Daniel Jenson, Jhonathan Navott, Piotr Grynfelder +4
Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic processes. While early architectures we…
cs.LG2024
Transformer Neural Processes - Kernel Regression
Daniel Jenson, Jhonathan Navott, Mengyan Zhang +3
Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic processes. Originally developed as a sc…