Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Tebbutt, Will"'
Autor:
Vaughan, Anna, Markou, Stratis, Tebbutt, Will, Requeima, James, Bruinsma, Wessel P., Andersson, Tom R., Herzog, Michael, Lane, Nicholas D., Chantry, Matthew, Hosking, J. Scott, Turner, Richard E.
Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning models have the potential to transform the complex weather prediction pipel
Externí odkaz:
http://arxiv.org/abs/2404.00411
Autor:
Ravuri, Aditya, Andersson, Tom R., Kazlauskaite, Ieva, Tebbutt, Will, Turner, Richard E., Hosking, J. Scott, Lawrence, Neil D., Kaiser, Markus
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 the ice disp
Externí odkaz:
http://arxiv.org/abs/2210.16568
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 support large num
Externí odkaz:
http://arxiv.org/abs/2106.10210
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 techniques to be a
Externí odkaz:
http://arxiv.org/abs/2101.07950
Autor:
Ashman, Matthew, So, Jonathan, Tebbutt, Will, Fortuin, Vincent, Pearce, Michael, Turner, Richard E.
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables
Externí odkaz:
http://arxiv.org/abs/2010.10177
Autor:
Bruinsma, Wessel P., Perim, Eric, Tebbutt, Will, Hosking, J. Scott, Solin, Arno, Turner, Richard E.
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 computational sc
Externí odkaz:
http://arxiv.org/abs/1911.06287
Autor:
Innes, Mike, Edelman, Alan, Fischer, Keno, Rackauckas, Chris, Saba, Elliot, Shah, Viral B, Tebbutt, Will
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 features of
Externí odkaz:
http://arxiv.org/abs/1907.07587
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 limited repre
Externí odkaz:
http://arxiv.org/abs/1802.07182
Autor:
Vaughan, Anna1 (AUTHOR) av555@cam.ac.uk, Tebbutt, Will1 (AUTHOR), Hosking, J. Scott2,3 (AUTHOR), Turner, Richard E.1 (AUTHOR)
Publikováno v:
Geoscientific Model Development. 2022, Vol. 15 Issue 1, p251-268. 18p.
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 limited repre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d79c82b3adc93291e3e49e953fbf7ad