2 papers
cs.LG2023
Reinterpreting survival analysis in the universal approximator age
Sören Dittmer, Michael Roberts, Jacobus Preller +4
Survival analysis is an integral part of the statistical toolbox. However, while most domains of classical statistics have embraced deep learning, survival analysis only recently g…
math.NA2023
Bayesian view on the training of invertible residual networks for solving linear inverse problems
Clemens Arndt, Sören Dittmer, Nick Heilenkötter +3
Learning-based methods for inverse problems, adapting to the data's inherent structure, have become ubiquitous in the last decade. Besides empirical investigations of their often r…