11 citations · 35 across the 11 of their papers we have counts for
11 papers
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 Group Equivariant Simplicial Message Passing Networks
Cong Liu, David Ruhe, Floor Eijkelboom +1
We introduce Clifford Group Equivariant Simplicial Message Passing Networks, a method for steerable E(n)-equivariant message passing on simplicial complexes. Our method integrates…
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…
Transient study using LoTSS -- framework development and preliminary results
Iris de Ruiter, Zachary S. Meyers, Antonia Rowlinson +3
We present a search for transient radio sources on time-scales of seconds to hours at 144 MHz using the LOFAR Two-metre Sky Survey (LoTSS). This search is conducted by examining sh…
On the Effectiveness of Hybrid Mutual Information Estimation
Marco Federici, David Ruhe, Patrick Forré
Estimating the mutual information from samples from a joint distribution is a challenging problem in both science and engineering. In this work, we realize a variational bound that…
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…
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
David Ruhe, Kaze Wong, Miles Cranmer +1
We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow. We first dem…
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…