5 citations · 8 across the 9 of their papers we have counts for
4 papers · 1 filter
Training of deep residual networks with stochastic MG/OPT
Cyrill von Planta, Alena Kopanicakova, Rolf Krause
We train deep residual networks with a stochastic variant of the nonlinear multigrid method MG/OPT. To build the multilevel hierarchy, we use the dynamical systems viewpoint specif…
Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks
Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal +4
Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information ca…
A Multilevel Approach to Training
Vanessa Braglia, Alena Kopaničáková, Rolf Krause
We propose a novel training method based on nonlinear multilevel minimization techniques, commonly used for solving discretized large scale partial differential equations. Our mult…
Multilevel Minimization for Deep Residual Networks
Lisa Gaedke-Merzhäuser, Alena Kopaničáková, Rolf Krause
We present a new multilevel minimization framework for the training of deep residual networks (ResNets), which has the potential to significantly reduce training time and effort. O…