16 citations · 18 across the 2 of their papers we have counts for
2 papers
stat.ML2020★ 2 cited
Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models
Juan Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls
Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications. They are widely used because they are simple, flexible and prov…
cs.LG2020★ 16 cited
Accounting for Input Noise in Gaussian Process Parameter Retrieval
J. Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls
Gaussian processes (GPs) are a class of Kernel methods that have shown to be very useful in geoscience and remote sensing applications for parameter retrieval, model inversion, and…