Search Results for author: Jose A. Rodríguez-Serrano

Found 3 papers, 1 papers with code

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.

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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