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Titlebook: Geometric Science of Information; 6th International Co Frank Nielsen,Frédéric Barbaresco Conference proceedings 2023 The Editor(s) (if appl

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發(fā)表于 2025-3-21 16:07:04 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Geometric Science of Information
副標題6th International Co
編輯Frank Nielsen,Frédéric Barbaresco
視頻videohttp://file.papertrans.cn/384/383603/383603.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Geometric Science of Information; 6th International Co Frank Nielsen,Frédéric Barbaresco Conference proceedings 2023 The Editor(s) (if appl
描述.This book constitutes the proceedings of the 6th International Conference on Geometric Science of Information, GSI 2023, held in St. Malo, France, during August 30-September 1, 2023...The 125 full papers presented in this volume were carefully reviewed and selected from 161 submissions. They cover all the main topics and highlights in the domain of geometric science of information, including information geometry manifolds of structured data/information and their advanced applications. The papers are organized in the following topics: geometry and machine learning; divergences and computational information geometry; statistics, topology and shape spaces; geometry and mechanics; geometry, learning dynamics and thermodynamics; quantum information geometry; geometry and biological structures; geometry and applications..
出版日期Conference proceedings 2023
關鍵詞Information theory; Information geometry; Thermodynamics; Machine learning; Riemannian geometry; Lie grou
版次1
doihttps://doi.org/10.1007/978-3-031-38271-0
isbn_softcover978-3-031-38270-3
isbn_ebook978-3-031-38271-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Geometric Science of Information978-3-031-38271-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Elaine Walker PhD,Karen M. Hochman MD3D curves in the visual scene as a sub-Riemannian structure. Horizontal curves in this setting express good continuation principles in 3D. Starting from the equation of neural activity we apply harmonic analysis techniques in the sub-Riemannian structure to solve the correspondence problem and find 3D percepts.
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https://doi.org/10.1007/978-3-642-74816-5 high-dimensional optimization problems on directed distances (divergences), under very non-restrictive (e.g. non-convex) constraints. Such a task can be comfortably achieved by the new . method of [., .]. In the present paper, we give some new insightful details on one cornerstone of this approach.
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