1 code implementation • 15 Oct 2021 • Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve.
no code implementations • 16 Apr 2021 • Daniel Heestermans Svendsen, Pablo Morales-Alvarez, Ana Belen Ruescas, Rafael Molina, Gustau Camps-Valls
Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations.
no code implementations • ICLR 2021 • Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato
Current approaches for uncertainty estimation in deep learning often produce too confident results.
no code implementations • 2 Oct 2017 • Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina, Gustau Camps-Valls
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources.