no code implementations • 8 Jun 2017 • Antonio D'Isanto, Kai Lars Polsterer
The presented method is extremely general and allows us to solve of any kind of probabilistic regression problems based on imaging data, for example estimating metallicity or star formation rate of galaxies.
Instrumentation and Methods for Astrophysics
no code implementations • 27 Mar 2018 • Antonio D'Isanto, Stefano Cavuoti, Fabian Gieseke, Kai Lars Polsterer
The methodology described here is very general and can be used to improve the performance of machine learning models for any regression or classification task.
Instrumentation and Methods for Astrophysics
no code implementations • 5 Mar 2021 • Kai Lars Polsterer, Antonio D'Isanto, Sebastian Lerch
We present what we achieved when using proper scoring rules to train deep neural networks as well as to evaluate the model estimates and how this work led from well calibrated redshift estimates to improvements in probabilistic weather forecasting.
Weather Forecasting Instrumentation and Methods for Astrophysics