Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Ruhe, David"'
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. In this work, we test a few novel message passing graph neural net
Externí odkaz:
http://arxiv.org/abs/2406.04052
Autor:
Zhdanov, Maksim, Ruhe, David, Weiler, Maurice, Lucic, Ana, Brandstetter, Johannes, Forré, Patrick
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of $\mathrm{E}(p, q)$-equivariant CNNs. CS-CNNs process multivector fields on pseudo-Euclidean spaces $\mathbb{R}^{p,q}$. They cover, for instance, $\mathrm{E}(3)$-e
Externí odkaz:
http://arxiv.org/abs/2402.14730
We introduce Clifford Group Equivariant Simplicial Message Passing Networks, a method for steerable E(n)-equivariant message passing on simplicial complexes. Our method integrates the expressivity of Clifford group-equivariant layers with simplicial
Externí odkaz:
http://arxiv.org/abs/2402.10011
Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data. These methods generally treat subsequent frames equally regarding the amount of noise in the diffusion process. Thi
Externí odkaz:
http://arxiv.org/abs/2402.09470
Autor:
de Ruiter, Iris, Meyers, Zachary S., Rowlinson, Antonia, Shimwell, Timothy W., Ruhe, David, Wijers, Ralph A. M. J.
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 short time-scale images derived from the LoTSS data. To allow imaging of
Externí odkaz:
http://arxiv.org/abs/2311.07394
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 generalizes both discriminative and generative approaches. Using this
Externí odkaz:
http://arxiv.org/abs/2306.00608
We introduce Clifford Group Equivariant Neural Networks: a novel approach for constructing $\mathrm{O}(n)$- and $\mathrm{E}(n)$-equivariant models. We identify and study the $\textit{Clifford group}$, a subgroup inside the Clifford algebra tailored t
Externí odkaz:
http://arxiv.org/abs/2305.11141
We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical systems. GCANs are based on symmetry group transformations using geometric (Clifford) algebras. We first review the quintessence of modern (plane-based) geometric algebra,
Externí odkaz:
http://arxiv.org/abs/2302.06594
We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow. We first demonstrate the merit of this method on illustrative experiments and then
Externí odkaz:
http://arxiv.org/abs/2211.09008
Autor:
Ruhe, David, Forré, Patrick
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 linearity approximation parameterized by neural networks. This comes a
Externí odkaz:
http://arxiv.org/abs/2107.13349