7 citations · 7 across the 5 of their papers we have counts for
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cs.LG2021
A framework for benchmarking uncertainty in deep regression
Franko Schmähling, Jörg Martin, Clemens Elster
We propose a framework for the assessment of uncertainty quantification in deep regression. The framework is based on regression problems where the regression function is a linear…
cs.LG2021★ 7 cited
Aleatoric uncertainty for Errors-in-Variables models in deep regression
Jörg Martin, Clemens Elster
A Bayesian treatment of deep learning allows for the computation of uncertainties associated with the predictions of deep neural networks. We show how the concept of Errors-in-Vari…
cs.LG2021
About exchanging expectation and supremum for conditional Wasserstein GANs
Jörg Martin
In cases where a Wasserstein GAN depends on a condition the latter is usually handled via an expectation within the loss function. Depending on the way this is motivated, the discr…