Search Results for author:
Found 9 papers, 8 papers with code
Aardvark Weather: end-to-end data-driven weather forecasting
Machine learning is revolutionising medium-range weather prediction.
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate.
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Pseudo-point approximations, one of the gold-standard methods for scaling GPs to large data sets, are well suited for handling off-the-grid spatial data.
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).
Sparse Gaussian Process Variational Autoencoders
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering.
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.
AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms
Stan's Hamilton Monte Carlo (HMC) has demonstrated remarkable sampling robustness and efficiency in a wide range of Bayesian inference problems through carefully crafted adaption schemes to the celebrated No-U-Turn sampler (NUTS) algorithm.
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data.
The Gaussian Process Autoregressive Regression Model (GPAR)
Multi-output regression models must exploit dependencies between outputs to maximise predictive performance.
