aiqbench analysis
This paper advances the theoretical understanding of deep linear networks by deriving explicit solutions for geodesics between full-rank matrices within their geometric structure. By identifying horizontal straight lines in the invariant balanced manifold that preserve geodesic properties under Riemannian submersion, it provides rigorous mathematical tools for analyzing optimization paths in high-dimensional parameter spaces.
Geodesics in the Deep Linear Network
arXiv:2510.07324v3 Announce Type: replace-cross Abstract: We derive a general system of ordinary differential equations and associated explicit solutions in a special case for geodesics between full rank matrices in the deep linear network geometry. In the process, we find horizontal straight lines in the invariant balanced manifold that remain geodesics under Riemannian submersion.
Abstract:We derive a general system of ordinary differential equations and associated explicit solutions in a special case for geodesics between full rank matrices in the deep linear network geometry. In the process, we find horizontal straight lines in the invariant balanced manifold that remain geodesics under Riemannian submersion.
Differential Geometry (math.DG); Machine Learning (cs.LG); Dynamical Systems (math.DS)
arXiv:2510.07324 [math.DG]
(or arXiv:2510.07324v3 [math.DG] for this version)
https://doi.org/10.48550/arXiv.2510.07324 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 146 words
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