Lie-group-type neural system learning by manifold retractions

DOI10.1016/j.neunet.2008.09.009zbMath1254.92009OpenAlexW2066024141WikidataQ51864644 ScholiaQ51864644MaRDI QIDQ1932122

Simone Fiori

Publication date: 17 January 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2008.09.009

zbMATH Keywords

Lie groupsmanifold retractionsneural learning theoryneural signal processing systems

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural biology (92C20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (3)

A possible neural representation of mathematical group structures ⋮ Averaging on manifolds by embedding algorithm ⋮ Sympnets: intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

Uses Software

• FastICA
• ICALAB

Cites Work

• Unnamed Item
• Minimizing a differentiable function over a differential manifold
• Runge-Kutta methods on Lie groups
• Trust-region methods on Riemannian manifolds
• A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold
• Newton's method on Riemannian manifolds and a geometric model for the human spine
• A Class of Intrinsic Schemes for Orthogonal Integration
• Optimization algorithms exploiting unitary constraints
• A Study on Neural Learning on Manifold Foliations: The Case of the Lie Group SU(3)
• The Gradient Projection Method Along Geodesics
• Riemannian geometry

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