5 papers
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
Yahya Aalaila, Mouad Elhamdi, Gerrit GroÃmann +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…
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…
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…
DeepRV: Accelerating Spatiotemporal Inference with Pre-trained Neural Priors
Jhonathan Navott, Daniel Jenson, Seth Flaxman +1
Gaussian Processes (GPs) provide a flexible and statistically principled foundation for modelling spatiotemporal phenomena, but their scaling makes them intractable for la…
Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing
Fan Yang, Sahoko Ishida, Mengyan Zhang +4
Remote sensing imagery offers rich spectral data across extensive areas for Earth observation. Many attempts have been made to leverage these data with transfer learning to develop…