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Advances in Data Science Women in Data Science and Mathematics (WiSDM) 2023 /

This volume features recent advances in data science ranging from algebraic geometry used for existence and uniqueness proofs of low rank approximations for tensor data, to category theory used for natural language processing applications, to approximation and optimization frameworks developed for c...

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Соавтор: SpringerLink (Online service)
Другие авторы: Garcia-Cardona, Cristina (Редактор), Lee, Harlin (Редактор)
Формат: Электронный ресурс eКнига
Язык:английский
Опубликовано: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Редактирование:1st ed. 2025.
Серии:Association for Women in Mathematics Series, 37
Предметы:
Artificial intelligence > Data processing.
Mathematics > Data processing.
Graph theory.
Quantitative research.
Pattern recognition systems.
Natural language processing (Computer science).
Data Science.
Computational Science and Engineering.
Graph Theory.
Data Analysis and Big Data.
Automated Pattern Recognition.
Natural Language Processing (NLP).
Online-ссылка:https://doi.org/10.1007/978-3-031-87804-6
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Итог:This volume features recent advances in data science ranging from algebraic geometry used for existence and uniqueness proofs of low rank approximations for tensor data, to category theory used for natural language processing applications, to approximation and optimization frameworks developed for convergence and robustness guarantees for deep neural networks. It provides ideas, methods, and tools developed in inherently interdisciplinary research problems requiring mathematics, computer science and data domain expertise. It also presents original results tackling real-world problems with immediate applications in industry and government. Contributions are based on the third Women in Data Science and Mathematics (WiSDM) Research collaboration Workshop that took place between August 7 and August 11, 2023 at the Institute for Pure & Applied Mathematics (IPAM) in Los Angeles, California, US. The submissions from the workshop and related groups constitute a valuable source for readers who are interested in mathematically-founded approaches to modeling data for exploration, understanding and prediction.
Объем:1 online resource (XIV, 357 p. 141 illus.). 116 illus. in color.
ISBN:9783031878046
ISSN:2364-5741 ;
DOI:10.1007/978-3-031-87804-6

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