Geometrical aspects of lattice gauge equivariant convolutional neural networks

no code implementations • 20 Mar 2023 • Jimmy Aronsson, David I. Müller, Daniel Schuh

Lattice gauge equivariant convolutional neural networks (L-CNNs) are a framework for convolutional neural networks that can be applied to non-Abelian lattice gauge theories without violating gauge symmetry.

Geometric Deep Learning and Equivariant Neural Networks

no code implementations • 28 May 2021 • Jan E. Gerken, Jimmy Aronsson, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson

We also discuss group equivariant neural networks for homogeneous spaces $\mathcal{M}=G/K$, which are instead equivariant with respect to the global symmetry $G$ on $\mathcal{M}$.

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Homogeneous vector bundles and $G$-equivariant convolutional neural networks

no code implementations • 12 May 2021 • Jimmy Aronsson

In this paper, we analyze GCNNs on homogeneous spaces $\mathcal{M} = G/K$ in the case of unimodular Lie groups $G$ and compact subgroups $K \leq G$.