Gupta nirupam (13 Ergebnisse)

paperback. Zustand: Good.

Paperback. Zustand: Brand New. 84 pages. 8.66x5.91x0.19 inches. In Stock.

Zustand: New. In English.

hardcover. Zustand: Very Good. Cover and edges may have some wear.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the 12th International Conference on Networked Systems, NETYS 2024, held in Rabat, Morocco, during May 29-31, 2024.The 14 full papers and 3 short papers presented were carefully reviewed and selected from 42 submissions.NETYS aims to bring together researchers and engineers from the theory and practice of distributed and networked systems. The scope of the conference covers all aspects related to the design and the development of networked systems, e.g.,cloud systems, formal verification, concurrent and distributed algorithms, data management, data science andparallel/concurrent/distributed programming. Additionally, this year NETYS also featured works on the emerging topic of distributed machine learning, alsocommonly referred to asfederated learning.…

Paperback. Zustand: Brand New. 276 pages. 9.25x6.10x9.21 inches. In Stock.

Taschenbuch. Zustand: Neu. Networked Systems | 12th International Conference, NETYS 2024, Rabat, Morocco, May 29-31, 2024, Proceedings | Armando Castañeda (u. a.) | Taschenbuch | Lecture Notes in Computer Science | x | Englisch | 2024 | Springer | EAN 9783031673207 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: New. In English.

Paperback. Zustand: Brand New. 2010 edition. 719 pages. 9.00x6.00x1.00 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms.Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms.Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area. …

Zustand: New.

Hardcover. Zustand: Brand New. 187 pages. 9.26x6.11x0.50 inches. In Stock.