Zobrazeno 1 - 10
of 13
pro vyhledávání: '"J. Emmanuel Johnson"'
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 3, p 035004 (2023)
Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions. Recent causal di
Externí odkaz:
https://doaj.org/article/b77f5758386b45ecbe44969acf841b8e
Publikováno v:
PLoS ONE, Vol 16, Iss 2, p e0246775 (2021)
[This corrects the article DOI: 10.1371/journal.pone.0235885.].
Externí odkaz:
https://doaj.org/article/5c4efc52f7a341d6bbb1b3296b92711c
Publikováno v:
PLoS ONE, Vol 15, Iss 10, p e0235885 (2020)
Kernel methods are powerful machine learning techniques which use generic non-linear functions to solve complex tasks. They have a solid mathematical foundation and exhibit excellent performance in practice. However, kernel machines are still conside
Externí odkaz:
https://doaj.org/article/ce6147816a024ed18359858825775c21
Autor:
Clara Burgard, Nicolas C Jourdain, Pierre Mathiot, Robin Smith, Rieke Schäfer, Justine Caillet, Tobias S. Finn, J. Emmanuel Johnson
Melt rates at the base of Antarctic ice shelves are needed to drive projections of the Antarctic ice sheet mass loss. Current basal melt parameterisations struggle to link open ocean properties to ice-shelf basal melt rates for the range of current s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74f0e8b3bdb2d27a8892188691369e61
https://doi.org/10.22541/essoar.168500298.88328432/v1
https://doi.org/10.22541/essoar.168500298.88328432/v1
The retrieval of atmospheric aerosol properties from satellite remote sensing is a complex and under-determined inverse problem. Traditional retrieval algorithms, based on radiative transfer models, must make approximations and assumptions to reach a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31e529efb0e0299701ea183adb1060fd
https://doi.org/10.5194/egusphere-egu23-7465
https://doi.org/10.5194/egusphere-egu23-7465
Autor:
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031170294
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is used to visualize similarities between po
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9da38eb9793b741b108ba1a0fb488c5b
https://doi.org/10.1007/978-3-031-17030-0_10
https://doi.org/10.1007/978-3-031-17030-0_10
Automatic anticipation and detection of extreme events constitute a major challenge in the current context of climate change. Machine learning approaches have excelled in detection of extremes and anomalies in Earth data cubes recently, but are typic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f7757ffc56cd89d3949b108e86ba782
https://doi.org/10.5194/egusphere-egu21-15729
https://doi.org/10.5194/egusphere-egu21-15729
Long-standing questions in multivariate statistics, information theory and machine learning reduce to estimating multivariate densities. However, this is still an unresolved problem and one of the biggest challenge in general, and for Earth system da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d69eef743b87496c6880f773c1816477
https://doi.org/10.5194/egusphere-egu21-14519
https://doi.org/10.5194/egusphere-egu21-14519
Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable. However, accurately estimating information content is challenging because
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75c791a71250bb43df71b71229595e99
http://arxiv.org/abs/2010.06476
http://arxiv.org/abs/2010.06476
Autor:
Gustau Camps-Valls, Adrian Perez-Suay, Valero Laparra, Miguel D. Mahecha, J. Emmanuel Johnson
Publikováno v:
PLoS ONE, Vol 15, Iss 10, p e0235885 (2020)
PLoS One
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PLOS ONE
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PLOS ONE
Kernel methods are powerful machine learning techniques which implement generic non-linear functions to solve complex tasks in a simple way. They Have a solid mathematical background and exhibit excellent performance in practice. However, kernel mach