Explorations in the Mathematics of Data Science The Inaugural Volume of the Center for Approximation and Mathematical Data Analytics /
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...
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| Muut tekijät: | , |
| Aineistotyyppi: | Elektroninen E-kirja |
| Kieli: | englanti |
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Cham :
Springer Nature Switzerland : Imprint: Birkhäuser,
2024.
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| Painos: | 1st ed. 2024. |
| Sarja: | Applied and Numerical Harmonic Analysis,
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| Aiheet: | |
| Linkit: | https://doi.org/10.1007/978-3-031-66497-7 |
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Sisällysluettelo:
- 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.