1 citations · 1 across the 3 of their papers we have counts for
5 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…