1 code implementation • 4 Aug 2023 • Laurens Sluijterman, Eric Cator, Tom Heskes
This paper introduces a first implementation of a novel likelihood-ratio-based approach for constructing confidence intervals for neural networks.
1 code implementation • 17 Feb 2023 • Laurens Sluijterman, Eric Cator, Tom Heskes
We demonstrate, both on toy examples and on a number of benchmark UCI regression data sets, that following the original recommendations and the novel separate regularization can lead to significant improvements.
1 code implementation • 22 Feb 2022 • Laurens Sluijterman, Eric Cator, Tom Heskes
A classical parametric model has uncertainty in the parameters due to the fact that the data on which the model is build is a random sample.
no code implementations • 12 Jul 2021 • Erwin de Gelder, Eric Cator, Jan-Pieter Paardekooper, Olaf Op den Camp, Bart De Schutter
In this paper, we propose a method to sample from a pdf estimated using KDE, such that the samples satisfy a linear equality constraint.
no code implementations • 7 Jun 2021 • Laurens Sluijterman, Eric Cator, Tom Heskes
We show why it is fundamentally flawed to test a prediction or confidence interval on a single test set.