Computing equivariant matrices on homogeneous spaces for geometric deep learning and automorphic Lie algebras

DOI10.1007/S10444-024-10126-7MaRDI QIDQ6495875

Vincent Knibbeler

Publication date: 2 May 2024

Published in: Advances in Computational Mathematics (Search for Journal in Brave)

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Harmonic analysis on homogeneous spaces (43A85) Computational aspects of associative rings (general theory) (16Z05) Applications of differential geometry to data and computer science (53Z50)

Cites Work

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• On the classification of automorphic Lie algebras
• The 1-2-3 of modular forms. Lectures at a summer school in Nordfjordeid, Norway, June 2004
• Classification of simple graded Lie algebras of finite growth
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• Homogeneous vector bundles and \(G\)-equivariant convolutional neural networks
• Higher-dimensional automorphic Lie algebras
• Reduction groups and automorphic Lie algebras
• On the Frobenius reciprocity theorem for locally compact groups
• Induced representation of locally compact groups. I
• Compact Lie Groups
• Reductions of integrable equations: dihedral group
• Crystal Statistics. I. A Two-Dimensional Model with an Order-Disorder Transition
• Finite Möbius groups, minimal immersions of spheres, and moduli

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