no code implementations • 6 Jun 2024 • Cong Liu, David Ruhe, Patrick Forré
Most current deep learning models equivariant to $O(n)$ or $SO(n)$ either consider mostly scalar information such as distances and angles or have a very high computational complexity.
1 code implementation • 22 Feb 2024 • Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of $\mathrm{E}(p, q)$-equivariant CNNs.
1 code implementation • 15 Feb 2024 • Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré
Experimental results show that our method is able to outperform both equivariant and simplicial graph neural networks on a variety of geometric tasks.
no code implementations • 12 Feb 2024 • David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom
Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data.
no code implementations • 1 Jun 2023 • 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.
1 code implementation • NeurIPS 2023 • David Ruhe, Johannes Brandstetter, Patrick Forré
We introduce Clifford Group Equivariant Neural Networks: a novel approach for constructing $\mathrm{O}(n)$- and $\mathrm{E}(n)$-equivariant models.
1 code implementation • 13 Feb 2023 • David Ruhe, Jayesh K. Gupta, Steven de Keninck, Max Welling, Johannes Brandstetter
GCANs are based on symmetry group transformations using geometric (Clifford) algebras.
1 code implementation • 15 Nov 2022 • David Ruhe, Kaze Wong, Miles Cranmer, Patrick Forré
We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow.
no code implementations • ICLR 2022 • David Ruhe, Patrick Forré
Additionally, using an approximate conditional independence, we can perform smoothing without having to parameterize a separate model.
1 code implementation • 20 Jun 2019 • David Ruhe, Giovanni Cinà, Michele Tonutti, Daan de Bruin, Paul Elbers
In this work we show how Bayesian modelling and the predictive uncertainty that it provides can be used to mitigate risk of misguided prediction and to detect out-of-domain examples in a medical setting.