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Found 3 papers, 1 papers with code
Deep Non-Crossing Quantiles through the Partial Derivative
Quantile Regression (QR) provides a way to approximate a single conditional quantile.
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.
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.
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