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…
Real-time small area estimation of food security in Zimbabwe: integrating mobile-phone and face-to-face surveys using joint multilevel regression and poststratification
Sahoko Ishida, Adam Howes, Valerie Bradley +14
Real-time, fine-grained monitoring of food security is essential for enabling timely and targeted interventions, thereby supporting the global goal of achieving zero hunger - a key…