67 citations · 108 across the 9 of their papers we have counts for
11 papers
Discretizing Group-Convolutional Neural Networks for 3D Geometry in Feature Space
Daniel Franzen, Jean Philip Filling, Michael Wand
Group-convolutional neural networks (GCNNs) are among the most important methods for introducing symmetry as an inductive bias in deep learning: In each linear layer, GCNNs sample…
On the Weight Dynamics of Deep Normalized Networks
Christian H. X. Ali Mehmeti-Göpel, Michael Wand
Recent studies have shown that high disparities in effective learning rates (ELRs) across layers in deep neural networks can negatively affect trainability. We formalize how these…
Nonlinearities in Steerable SO(2)-Equivariant CNNs
Daniel Franzen, Michael Wand
Invariance under symmetry is an important problem in machine learning. Our paper looks specifically at equivariant neural networks where transformations of inputs yield homomorphic…
Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability
Marc Stieffenhofer, Tristan Bereau, Michael Wand
Switching between different levels of resolution is essential for multiscale modeling, but restoring details at higher resolution remains challenging. In our previous study we have…
Adversarial Reverse Mapping of Equilibrated Condensed-Phase Molecular Structures
Marc Stieffenhofer, Michael Wand, Tristan Bereau
A tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed m…
Deep Non-Line-of-Sight Reconstruction
Javier Grau Chopite, Matthias B. Hullin, Michael Wand +1
The recent years have seen a surge of interest in methods for imaging beyond the direct line of sight. The most prominent techniques rely on time-resolved optical impulse responses…