16 citations · 36 across the 6 of their papers we have counts for
7 papers
Multi-level Neural Networks for high-dimensional parametric obstacle problems
Martin Eigel, Cosmas Heiß, Janina E. Schütte
A new method to solve computationally challenging (random) parametric obstacle problems is developed and analyzed, where the parameters can influence the related partial differenti…
Multilevel CNNs for Parametric PDEs
Cosmas Heiß, Ingo Gühring, Martin Eigel
We combine concepts from multilevel solvers for partial differential equations (PDEs) with neural network based deep learning and propose a new methodology for the efficient numeri…
On Assignment Problems Related to Gromov-Wasserstein Distances on the Real Line
Robert Beinert, Cosmas Heiss, Gabriele Steidl
Let and , , be real numbers. We show by an example that the assignment problem $$ \max_{σ\in S_n} F_σ(x,y) := \frac12 \sum_{…
Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition
Philipp Flotho, Cosmas Heiss, Gabriele Steidl +1
Microexpressions are fast and spatially small facial expressions that are difficult to detect. Therefore motion magnification techniques, which aim at amplifying and hence revealin…
Self-Supervised Equivariant Scene Synthesis from Video
Cinjon Resnick, Or Litany, Cosmas Heiß +3
We propose a self-supervised framework to learn scene representations from video that are automatically delineated into background, characters, and their animations. Our method cap…
In-Distribution Interpretability for Challenging Modalities
Cosmas Heiß, Ron Levie, Cinjon Resnick +2
It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches. However, the development of methods to investigate the m…
Interval Neural Networks: Uncertainty Scores
Luis Oala, Cosmas Heiß, Jan Macdonald +3
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network. T…