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Română · beta. Traducerea a fost făcută automat și este încă în curs de revizuire. Citește pagina în engleză

perspectiva aiqbenchMachine learning

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This paper is mainly mathematical: it develops projection-based, epsilon-perturbed variational methods on the unit hypersphere. Its ML relevance comes from framing attention in Transformers as a flow map in the tangent fiber of each token. Readers interested in the theory behind attention may find the geometric view useful, but the paper offers no empirical results or practical architecture changes, so its near-term impact on ML practice is unclear.
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Ce este aceastaLectura unui model asupra articolului și a acoperirii conexe, scrisă pentru aiqbench. Nu sunt cuvintele publicației.

Inexact calculus of variations on the hyperspherical tangent bundle with connections to the attention mechanism

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2507.15431v5 Announce Type: replace Abstract: We offer a theoretical mathematical background through Lagrangian optimization on the unit hyperspherical manifold and its tangential structure. Our methods can be categorized as inexact since our methods are projection-based and since we will perturb the functional optimization with epsilon-type quantities. We draw connections to the attention mechanism and the Transformer since it exists as a flow map in the tangent fiber for each token along the high-dimensional unit sphere. Our motivation for this work is primarily twofold: we study the attention mechanism under its flow map and its relations to traditional calculus of variations and Lagrangian optimization; and we study several calculus problems of variations on the unit hypersphere that appeal to a broader mathematical lens in approximating, variational contexts.

Abstract:We offer a theoretical mathematical background through Lagrangian optimization on the unit hyperspherical manifold and its tangential structure. Our methods can be categorized as inexact since our methods are projection-based and since we will perturb the functional optimization with epsilon-type quantities. We draw connections to the attention mechanism and the Transformer since it exists as a flow map in the tangent fiber for each token along the high-dimensional unit sphere. Our motivation for this work is primarily twofold: we study the attention mechanism under its flow map and its relations to traditional calculus of variations and Lagrangian optimization; and we study several calculus problems of variations on the unit hypersphere that appeal to a broader mathematical lens in approximating, variational contexts.

Machine Learning (cs.LG)

arXiv:2507.15431 [cs.LG]

(or arXiv:2507.15431v5 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2507.15431 Focus to learn more arXiv-issued DOI via DataCite

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