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Explorations in the Mathematics of Data Science de Simon Foucart

Explorations in the Mathematics of Data Science: Applied and Numerical Harmonic Analysis

Editat de Simon Foucart, Stephan Wojtowytsch
en Limba Engleză Hardback – 13 sep 2024
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.
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Specificații

ISBN-13: 9783031664960
ISBN-10: 3031664965
Pagini: 300
Ilustrații: XIV, 206 p. 13 illus., 10 illus. in color.
Dimensiuni: 160 x 241 x 22 mm
Greutate: 0.62 kg
Ediția:2024
Editura: birkhäuser
Colecția Applied and Numerical Harmonic Analysis
Seria Applied and Numerical Harmonic Analysis

Locul publicării:Cham, Switzerland

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

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 foundations of data science, especially those in Approximation Theory