30 citations · 38 across the 6 of their papers we have counts for
6 papers
Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
J. Emmanuel Johnson, Redouane Lguensat, Ronan Fablet +2
Optimal Interpolation (OI) is a widely used, highly trusted algorithm for interpolation and reconstruction problems in geosciences. With the influx of more satellite missions, we h…
The Kernelized Taylor Diagram
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse +4
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely u…
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks
J. Emmanuel Johnson, Sairam Sundaresan, Tansu Daylan +6
Magnetic activity in stars manifests as dark spots on their surfaces that modulate the brightness observed by telescopes. These light curves contain important information on stella…
Information Theory in Density Destructors
J. Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls +2
Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy). Multiva…
Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
J. Emmanuel Johnson, Valero Laparra, Maria Piles +1
Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable. Howe…
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences
J. Emmanuel Johnson, Valero Laparra, Adrián Pérez-Suay +2
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 backgrou…