66 citations · 132 across the 11 of their papers we have counts for
4 papers · 1 filter
Deep and interpretable regression models for ordinal outcomes
Lucas Kook, Lisa Herzog, Torsten Hothorn +2
Outcomes with a natural order commonly occur in prediction tasks and often the available input data are a mixture of complex data like images and tabular predictors. Deep Learning…
Integrating uncertainty in deep neural networks for MRI based stroke analysis
Lisa Herzog, Elvis Murina, Oliver Dürr +2
At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the models uncertainty. However, a quantification of the reliabili…
Single Shot MC Dropout Approximation
Kai Brach, Beate Sick, Oliver Dürr
Deep neural networks (DNNs) are known for their high prediction performance, especially in perceptual tasks such as object recognition or autonomous driving. Still, DNNs are prone…
Deep transformation models: Tackling complex regression problems with neural network based transformation models
Beate Sick, Torsten Hothorn, Oliver Dürr
We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks, it is pr…