Juan Emmanuel Johnson

According to our database, Juan Emmanuel Johnson authored at least 15 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of five.

2025

Operational machine learning for remote spectroscopic detection of CH₄ point sources.

Vít Ruzicka, Gonzalo Mateo-García, Itziar Irakulis-Loitxate, Juan Emmanuel Johnson, Manuel Montesino San Martín, Anna Allen, Luis Guanter, David R. Thompson

CoRR, November, 2025

Estimating Information Theoretic Measures via Multidimensional Gaussianization.

Valero Laparra, Juan Emmanuel Johnson, Gustau Camps-Valls, Raúl Santos-Rodríguez, Jesús Malo

IEEE Trans. Pattern Anal. Mach. Intell., February, 2025

2024

SPDE priors for uncertainty quantification of end-to-end neural data assimilation schemes.

Maxime Beauchamp, Nicolas Desassis, J. Emmanuel Johnson, Simon Benaïchouche, Pierre Tandeo, Ronan Fablet

CoRR, 2024

2023

Learning latent functions for causal discovery.

Emiliano Diaz, Gherardo Varando, Juan Emmanuel Johnson, Gustau Camps-Valls

Mach. Learn. Sci. Technol., September, 2023

OceanBench: The Sea Surface Height Edition.

J. Emmanuel Johnson, Quentin Febvre, Anastasiia Gorbunova, Sammy Metref, Maxime Ballarotta, Julien Le Sommer, Ronan Fablet

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Learning Relevant Features of Optical Water Types.

Katalin Blix, Ana Belen Ruescas, Juan Emmanuel Johnson, Gustau Camps-Valls

IEEE Geosci. Remote. Sens. Lett., 2022

Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables.

J. Emmanuel Johnson, Redouane Lguensat, Ronan Fablet, Emmanuel Cosme, Julien Le Sommer

CoRR, 2022

The Kernelized Taylor Diagram.

Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen

Proceedings of the Nordic Artificial Intelligence Research and Development, 2022

Orthonormal Convolutions for the Rotation Based Iterative Gaussianization.

Valero Laparra, Alexander Hepburn, Juan Emmanuel Johnson, Jesús Malo

Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

2020

Accounting for Input Noise in Gaussian Process Parameter Retrieval.

Juan Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls

IEEE Geosci. Remote. Sens. Lett., 2020

RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks.

Juan Emmanuel Johnson, Sairam Sundaresan, Tansu Daylan, Lisseth Gavilan, Daniel K. Giles, Stela Ishitani Silva, Anna Jungbluth, Brett M. Morris, Andrés Muñoz-Jaramillo

CoRR, 2020

Information Theory in Density Destructors.

Juan Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls, Raúl Santos-Rodríguez, Jesús Malo

CoRR, 2020

Information Theory Measures via Multidimensional Gaussianization.

Valero Laparra, Juan Emmanuel Johnson, Gustau Camps-Valls, Raúl Santos-Rodríguez, Jesus Malo

CoRR, 2020

Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences.

Juan Emmanuel Johnson, Valero Laparra, Adrián Pérez-Suay, Miguel D. Mahecha, Gustau Camps-Valls

CoRR, 2020

2018

Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models.

Juan Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls

Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018