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arXiv:2409.12057 (stat)
[Submitted on 18 Sep 2024 (v1) , last revised 6 Nov 2024 (this version, v2)]

Title: Cartan moving frames and the data manifolds

Title: 嘉当活动框架与数据流形

Authors:Eliot Tron, Rita Fioresi, Nicolas Couellan, Stéphane Puechmorel
Abstract: The purpose of this paper is to employ the language of Cartan moving frames to study the geometry of the data manifolds and its Riemannian structure, via the data information metric and its curvature at data points. Using this framework and through experiments, explanations on the response of a neural network are given by pointing out the output classes that are easily reachable from a given input. This emphasizes how the proposed mathematical relationship between the output of the network and the geometry of its inputs can be exploited as an explainable artificial intelligence tool.
Abstract: 本文的目的是利用嘉当活动标架的语言,通过数据信息度量及其在数据点处的曲率来研究数据流形及其黎曼结构的几何性质。利用这一框架并通过实验,通过对从给定输入容易到达的输出类别加以指明,给出了神经网络响应的解释。这强调了如何将网络输出与输入几何之间的所提出的数学关系作为可解释的人工智能工具加以利用。
Subjects: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Differential Geometry (math.DG)
Cite as: arXiv:2409.12057 [stat.ML]
  (or arXiv:2409.12057v2 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.2409.12057
arXiv-issued DOI via DataCite
Related DOI: https://doi.org/10.1007/s41884-024-00159-8
DOI(s) linking to related resources

Submission history

From: Eliot Tron [view email]
[v1] Wed, 18 Sep 2024 15:31:29 UTC (134 KB)
[v2] Wed, 6 Nov 2024 17:13:51 UTC (213 KB)
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