1 code implementation • 24 Feb 2023 • Nataša Tagasovska, Firat Ozdemir, Axel Brando
Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge.
no code implementations • 30 Jan 2022 • Axel Brando, Joan Gimeno, Jose A. Rodríguez-Serrano, Jordi Vitrià
Quantile Regression (QR) provides a way to approximate a single conditional quantile.
no code implementations • 1 Jan 2021 • Axel Brando, Joan Gimeno, Jose Antonio Rodriguez-Serrano, Jordi Vitria
Most of the predictive systems currently in use do not report any useful information for auditing their associated uncertainty and evaluating the corresponding risk.
1 code implementation • NeurIPS 2019 • Axel Brando, Jose A. Rodríguez-Serrano, Jordi Vitrià, Alberto Rubio
In this paper, we propose a generic deep learning framework that learns an Uncountable Mixture of Asymmetric Laplacians (UMAL), which will allow us to estimate heterogeneous distributions of the output variable and shows its connections to quantile regression.
no code implementations • 24 Jul 2018 • Axel Brando, Jose A. Rodríguez-Serrano, Mauricio Ciprian, Roberto Maestre, Jordi Vitrià
Deep Learning is a consolidated, state-of-the-art Machine Learning tool to fit a function when provided with large data sets of examples.