Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians from forecasting modelling Watch Video

⏲ Duration: 1 min 80 sec
✓ Published: 28-Oct-2019

Description:
In regression tasks, aleatoric uncertainty is commonly addressed by considering a parametric distribution of the output variable, which is based on strong assumptions such as symmetry, unimodality or by supposing a restricted shape. These assumptions are too limited in scenarios where complex shapes, strong skews or multiple modes are present. In this paper, we propose a generic deep learning framework that learns an Uncountable Mixture of Asymmetric Laplacians (UMAL), which will allow us to est