2 citations · 4 across the 3 of their papers we have counts for
7 papers
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
Leo Schwinn, Leon Bungert, An Nguyen +5
The reliability of neural networks is essential for their use in safety-critical applications. Existing approaches generally aim at improving the robustness of neural networks to e…
Neural Architecture Search via Bregman Iterations
Leon Bungert, Tim Roith, Daniel Tenbrinck +1
We propose a novel strategy for Neural Architecture Search (NAS) based on Bregman iterations. Starting from a sparse neural network our gradient-based one-shot algorithm gradually…
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis
Leo Schwinn, An Nguyen, René Raab +5
The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in sa…
Variational regularisation for inverse problems with imperfect forward operators and general noise models
Leon Bungert, Martin Burger, Yury Korolev +1
We study variational regularisation methods for inverse problems with imperfect forward operators whose errors can be modelled by order intervals in a partial order of a Banach lat…
Structural analysis of an -infinity variational problem and relations to distance functions
Leon Bungert, Yury Korolev, Martin Burger
In this work we analyse the functional defined on Lipschitz functions with homogeneous Dirichlet boundary conditions. Our analysis is performed di…
Asymptotic Profiles of Nonlinear Homogeneous Evolution Equations of Gradient Flow Type
Leon Bungert, Martin Burger
This work is concerned with the gradient flow of absolutely -homogeneous convex functionals on a Hilbert space, which we show to exhibit finite () or infinite extinction ti…