5 papers
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…
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…
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…
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…
Strong continuity for the 2D Euler equations
Gianluca Crippa, Elizaveta Semenova, Stefano Spirito
We prove two results of strong continuity with respect to the initial datum for bounded solutions to the Euler equations in vorticity form. The first result provides sequential con…