2.2k citations · 4k across the 42 of their papers we have counts for
45 papers
KidSat: satellite imagery to map childhood poverty dataset and benchmark
Makkunda Sharma, Fan Yang, Duy-Nhat Vo +6
Satellite imagery has emerged as an important tool to analyse demographic, health, and development indicators. While various deep learning models have been built for these tasks, e…
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya
Elizaveta Semenova, Swapnil Mishra, Samir Bhatt +2
Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the st…
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
Elizaveta Semenova, Prakhar Verma, Max Cairney-Leeming +3
Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs). These learned generato…
City-Wide Perceptions of Neighbourhood Quality using Street View Images
Emily Muller, Emily Gemmell, Ishmam Choudhury +6
The interactions of individuals with city neighbourhoods is determined, in part, by the perceived quality of urban environments. Perceived neighbourhood quality is a core component…
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Xenia Miscouridou, Samir Bhatt, George Mohler +2
Hawkes processes are point process models that have been used to capture self-excitatory behavior in social interactions, neural activity, earthquakes and viral epidemics. They can…
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
Alexander Terenin, David R. Burt, Artem Artemev +4
Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent…