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Explorations in the mathematics of d...

Foucart, Simon.

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Explorations in the mathematics of data science = the inaugural volume of the Center for Approximation and Mathematical Data Analytics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Explorations in the mathematics of data science/ edited by Simon Foucart, Stephan Wojtowytsch.
其他題名:	the inaugural volume of the Center for Approximation and Mathematical Data Analytics /
其他作者:	Foucart, Simon.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xiv, 286 p. :ill. (chiefly color), digital ;24 cm.
內容註:	Preface -- S-Procedure Relaxation: a Case of Exactness Involving Chebyshev Centers -- Neural networks: deep, shallow, or in between? -- Qualitative neural network approximation over R and C -- Linearly Embedding Sparse Vectors from l2 to l1 via Deterministic Dimension-Reducing Maps -- Ridge Function Machines -- Learning Collective Behaviors from Observation -- Provably Accelerating Ill-Conditioned Low-Rank Estimation via Scaled Gradient Descent, Even with Overparameterization -- CLAIRE: Scalable GPU-Accelerated Algorithms for Diffeomorphic Image Registration in 3D -- A genomic tree based sparse solver -- A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces.
Contained By:	Springer Nature eBook
標題:	Approximation theory. -
電子資源:	https://doi.org/10.1007/978-3-031-66497-7
ISBN:	9783031664977

Explorations in the mathematics of data science = the inaugural volume of the Center for Approximation and Mathematical Data Analytics /
Explorations in the mathematics of data sciencethe inaugural volume of the Center for Approximation and Mathematical Data Analytics /[electronic resource] :edited by Simon Foucart, Stephan Wojtowytsch. - Cham :Springer Nature Switzerland :2024. - xiv, 286 p. :ill. (chiefly color), digital ;24 cm. - Applied and numerical harmonic analysis,2296-5017. - Applied and numerical harmonic analysis..

Preface -- S-Procedure Relaxation: a Case of Exactness Involving Chebyshev Centers -- Neural networks: deep, shallow, or in between? -- Qualitative neural network approximation over R and C -- Linearly Embedding Sparse Vectors from l2 to l1 via Deterministic Dimension-Reducing Maps -- Ridge Function Machines -- Learning Collective Behaviors from Observation -- Provably Accelerating Ill-Conditioned Low-Rank Estimation via Scaled Gradient Descent, Even with Overparameterization -- CLAIRE: Scalable GPU-Accelerated Algorithms for Diffeomorphic Image Registration in 3D -- A genomic tree based sparse solver -- A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces.

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.

ISBN: 9783031664977

Standard No.: 10.1007/978-3-031-66497-7doiSubjects--Topical Terms:

628068
Approximation theory.

LC Class. No.: QA221

Dewey Class. No.: 511.4

Explorations in the mathematics of data science = the inaugural volume of the Center for Approximation and Mathematical Data Analytics /
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