11 citations · 35 across the 11 of their papers we have counts for
7 papers · 1 filter
Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks
Cong Liu, David Ruhe, Patrick Forré
Most current deep learning models equivariant to or either consider mostly scalar information such as distances and angles or have a very high computational complexi…
Clifford-Steerable Convolutional Neural Networks
Maksim Zhdanov, David Ruhe, Maurice Weiler +3
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of -equivariant CNNs. CS-CNNs process multivector fields on pseudo-Euclidean…
Rolling Diffusion Models
David Ruhe, Jonathan Heek, Tim Salimans +1
Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data. These methods generally treat subsequent fram…
Clifford Group Equivariant Neural Networks
David Ruhe, Johannes Brandstetter, Patrick Forré
We introduce Clifford Group Equivariant Neural Networks: a novel approach for constructing - and -equivariant models. We identify and study the $\text…
Geometric Clifford Algebra Networks
David Ruhe, Jayesh K. Gupta, Steven de Keninck +2
We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical systems. GCANs are based on symmetry group transformations using geometric (Clifford) algebras. We fir…
Self-Supervised Inference in State-Space Models
David Ruhe, Patrick Forré
We perform approximate inference in state-space models with nonlinear state transitions. Without parameterizing a generative model, we apply Bayesian update formulas using a local…
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
David Ruhe, Giovanni Cinà, Michele Tonutti +2
The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to provide valuable assistance in clinical decision making. Classical machine learni…