Search Results for author: Axel Brando

Found 5 papers, 2 papers with code

Retrospective Uncertainties for Deep Models using Vine Copulas

1 code implementation24 Feb 2023 Nataša Tagasovska, Firat Ozdemir, Axel Brando

Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge.


Deep Non-Crossing Quantiles through the Partial Derivative

no code implementations30 Jan 2022 Axel Brando, Joan Gimeno, Jose A. Rodríguez-Serrano, Jordi Vitrià

Quantile Regression (QR) provides a way to approximate a single conditional quantile.

ChePAN: Constrained Black-Box Uncertainty Modelling with Quantile Regression

no code implementations1 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.


Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians

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.

Decision Making regression

Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series

no code implementations24 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.

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