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Titlebook: Explorations in the Mathematics of Data Science; The Inaugural Volume Simon Foucart,Stephan Wojtowytsch Book 2024 The Editor(s) (if applica

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書目名稱Explorations in the Mathematics of Data Science
副標(biāo)題The Inaugural Volume
編輯Simon Foucart,Stephan Wojtowytsch
視頻videohttp://file.papertrans.cn/321/320223/320223.mp4
概述Explores the most recent developments in the mathematics of data science.Highlights the activities of the Center for Approximation and Mathematical Data Analytics (CAMDA).Focuses on the theoretical fo
叢書名稱Applied and Numerical Harmonic Analysis
圖書封面Titlebook: Explorations in the Mathematics of Data Science; The Inaugural Volume Simon Foucart,Stephan Wojtowytsch Book 2024 The Editor(s) (if applica
描述.This edited volume reports on the recent activities of the new Center for Approximation and Mathematical Data Analytics (CAMDA) at Texas A&M University. Chapters are based on talks from CAMDA’s inaugural conference – held in May 2023 – and its seminar series, as well as work performed by members of the Center. They showcase the interdisciplinary nature of data science, emphasizing its mathematical and theoretical foundations, especially those rooted in approximation theory..
出版日期Book 2024
關(guān)鍵詞Approximation Theory; Learning Theory; Compressive Sensing; Neural Networks; Center for Approximation an
版次1
doihttps://doi.org/10.1007/978-3-031-66497-7
isbn_softcover978-3-031-66499-1
isbn_ebook978-3-031-66497-7Series ISSN 2296-5009 Series E-ISSN 2296-5017
issn_series 2296-5009
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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S-procedure Relaxation: A Case of Exactness Involving Chebyshev Centers,particular instance where the quadratic constraints involve orthogonal projectors. Our argument exploits a previous work of ours, where exact Chebyshev centers were obtained in a different way. We conclude by stating some open questions and by commenting on other recent results in optimal recovery.
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CLAIRE: Scalable GPU-Accelerated Algorithms for Diffeomorphic Image Registration in 3D,llelism and deploy our code on modern high-performance computing architectures. Our solver allows us to solve clinically relevant problems in under four seconds on a single GPU. It can also be applied to large-scale 3D imaging applications with data that is discretized on meshes with billions of vox
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S-procedure Relaxation: A Case of Exactness Involving Chebyshev Centers,ions on the functions to be learned. Working in a finite-dimensional Hilbert space, we consider model assumptions based on approximability and observation inaccuracies modeled as additive errors bounded in .. We focus on the local recovery problem, which amounts to the determination of Chebyshev cen
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