Search Results for author: J. Scott Hosking

Found 9 papers, 7 papers with code

Sea ice detection using concurrent multispectral and synthetic aperture radar imagery

no code implementations11 Jan 2024 Martin S J Rogers, Maria Fox, Andrew Fleming, Louisa van Zeeland, Jeremy Wilkinson, J. Scott Hosking

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.

Image Segmentation Semantic Segmentation

Autoregressive Conditional Neural Processes

1 code implementation25 Mar 2023 Wessel P. Bruinsma, Stratis Markou, James Requiema, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner

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.


Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model

1 code implementation28 Mar 2022 Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, Damon J. Wischik

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.

BIG-bench Machine Learning

Convolutional conditional neural processes for local climate downscaling

1 code implementation20 Jan 2021 Anna Vaughan, Will Tebbutt, J. Scott Hosking, Richard E. Turner

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs).

Gaussian Processes

Scalable Exact Inference in Multi-Output Gaussian Processes

1 code implementation ICML 2020 Wessel P. Bruinsma, Eric Perim, Will Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner

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.

Gaussian Processes

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