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This research establishes a rigorous mathematical link between advanced differential geometry and the structural properties of balanced manifolds in deep learning architectures. By proving that specific matrix generalizations form exact special Lagrangian manifolds, the study offers new theoretical frameworks for understanding optimization landscapes in machine learning.
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Special Lagrangian cones in Deep Learning

arXiv1 min read

arXiv:2609.20159v1 Announce Type: cross Abstract: We introduce a matrix generalization of the cone of Harvey and Lawson and prove that it is an exact special Lagrangian manifold. We further show that it belongs to a family of exact special Lagrangian manifolds that foliate the balanced manifold arising in deep learning.

Abstract:We introduce a matrix generalization of the cone of Harvey and Lawson and prove that it is an exact special Lagrangian manifold. We further show that it belongs to a family of exact special Lagrangian manifolds that foliate the balanced manifold arising in deep learning.

Differential Geometry (math.DG); Machine Learning (cs.LG); Symplectic Geometry (math.SG)

arXiv:2609.20159 [math.DG]

(or arXiv:2609.20159v1 [math.DG] for this version)

https://doi.org/10.48550/arXiv.2609.20159 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 145 words

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