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View article: Reclassifying Lethal Heat
Reclassifying Lethal Heat Open
As heatwaves increase in both frequency and intensity globally, the need to develop tools to predict the human impact and develop a more comprehensive understanding of the impact mechanism at a population level is becoming more urgent. Our…
View article: End-to-end data-driven weather forecasting. (source code, sample data and trained models)
End-to-end data-driven weather forecasting. (source code, sample data and trained models) Open
This resource contains the key source code, some sample data and the trained models from the paper: "End-to-end data-driven weather forecasting."
View article: Ice Core Dating using Probabilistic Programming
Ice Core Dating using Probabilistic Programming Open
Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a function of depth. Under certain conditions, chemicals locked in t…
View article: Convolutional conditional neural processes for local climate downscaling
Convolutional conditional neural processes for local climate downscaling Open
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniq…
View article: Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes Open
Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temporal modelling problems such as those in climate science and epidemiology. However, existing GP approximations do not simultaneously suppor…
View article: Convolutional conditional neural processes for local climatedownscaling
Convolutional conditional neural processes for local climatedownscaling Open
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep learning techniq…
View article: Convolutional conditional neural processes for local climate downscaling
Convolutional conditional neural processes for local climate downscaling Open
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep learning techniq…
View article: Deep learning for monthly Arctic sea ice concentration prediction
Deep learning for monthly Arctic sea ice concentration prediction Open
<p>Over recent decades, the Arctic has warmed faster than any region on Earth. The rapid decline in Arctic sea ice extent (SIE) is often highlighted as a key indicator of anthropogenic climate change. Changes in sea ice disrupt Arcti…
View article: Scalable Exact Inference in Multi-Output Gaussian Processes
Scalable Exact Inference in Multi-Output Gaussian Processes Open
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. The key problem with MOGPs is their compu…
View article: Zygote: A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
Zygote: A Differentiable Programming System to Bridge Machine Learning and Scientific Computing Open
Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data. At the same time, machine learning models are becoming increasingly sophisticated and exhibit many …
View article: A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing Open
Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data. At the same time, machine learning models are becoming increasingly sophisticated and exhibit many …
View article: The Gaussian Process Autoregressive Regression Model (GPAR)
The Gaussian Process Autoregressive Regression Model (GPAR) Open
Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have li…