Search Results for author:
Found 9 papers, 7 papers with code
Aardvark Weather: end-to-end data-driven weather forecasting
Machine learning is revolutionising medium-range weather prediction.
Sea ice detection using concurrent multispectral and synthetic aperture radar imagery
As the spatial-temporal coverage of MSI and SAR imagery continues to increase, ViSual\_IceD provides a new opportunity for robust, accurate sea ice coverage detection in polar regions.
Autoregressive Conditional Neural Processes
Our work provides an example of how ideas from neural distribution estimation can benefit neural processes, and motivates research into the AR deployment of other neural process models.
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate.
Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model
The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization.
Convolutional conditional neural processes for local climate downscaling
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs).
Ensembling geophysical models with Bayesian Neural Networks
Ensembles of geophysical models improve projection accuracy and express uncertainties.
Scalable Exact Inference in Multi-Output Gaussian Processes
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling.
