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Found 5 papers, 2 papers with code
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians
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.
Retrospective Uncertainties for Deep Models using Vine Copulas
Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge.
Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series
Deep Learning is a consolidated, state-of-the-art Machine Learning tool to fit a function when provided with large data sets of examples.
ChePAN: Constrained Black-Box Uncertainty Modelling with Quantile Regression
Most of the predictive systems currently in use do not report any useful information for auditing their associated uncertainty and evaluating the corresponding risk.
Deep Non-Crossing Quantiles through the Partial Derivative
Quantile Regression (QR) provides a way to approximate a single conditional quantile.
