Gupta nirupam (42 résultats)

paperback. Etat : Good.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

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

Etat : New.

Etat : New. In English.

Etat : As New. Unread book in perfect condition.

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

Etat : New.

Taschenbuch. Etat : 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. Etat : Brand New. 276 pages. 9.25x6.10x9.21 inches. In Stock.

Etat : New.

Paperback. Etat : new. Paperback. ThisLNCSvolumecontainsthepaperspresentedatthe8thSimulatedEvolution and Learning (SEAL 2010) Conference held during December 1-4, 2010 at the Indian Institute of Technology Kanpur in India. SEAL is a prestigious inter- tional conference series in evolutionaryoptimization and machine learning. This biennial event started in Seoul, South Korea in 1996 and was thereafter held in Canberra, Australia in 1998, Nagoya, Japan in 2000, Singapore in 2002, Busan, South Korea in 2004,Hefei, China in 2006and Melbourne, Australia in 2008. SEAL 2010 received 141 paper submissions in total from 30 countries. After a rigorous peer-review process involving 431 reviews in total (averaging a little morethan3reviewsperpaper),60full-lengthand19shortpaperswereaccepted for presentation (both oral and poster) at the conference. The full-length papers alonecorrespondtoa42. 6%acceptancerateandshortpapersaddanother13. 5%. ThepapersincludedinthisLNCSvolumecoverawiderangeoftopicsinsi- latedevolutionandlearning. Theacceptedpapershavebeenclassi?edintothef- lowingmaincategories:(a)theoreticaldevelopments,(b)evolutionaryalgorithms andapplications,(c)learningmethodologies,(d)multi-objectiveevolutionary- gorithms and applications,(e) hybrid algorithms and (f) industrial applications.The conference featured three distinguished keynote speakers. Narendra Karmarkar's talk on "Beyond Convexity: New Perspectives in Computational Optimization" focused on providing new theoretical concepts for non-convex optimization and indicated a rich connection between optimization and ma- ematical physics and also showed a deep signi?cance of advanced geometry to optimization. The advancement of optimization theory for non-convex problems is bene?cial for meta-heuristic optimization algorithms such as evolutionary - gorithms. Manindra Agrawal's talk on "PRIMES is in P" provided a mu- improved version of his celebrated and ground-breaking 2002 work on poly- mial time algorithm for testing prime numbers. The theoretical computation work presented in this keynote lecture should be motivating for the evolutionary optimization and machine learning community at large. Constitutes the proceedings of the 8th International Conference on Simulated Evolution and Learning, SEAL 2010, held in Kanpur, India, in December 2010. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Etat : 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. …

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. In English.

Etat : New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.

Etat : As New. Unread book in perfect condition.

Hardback. Etat : New. 2024 ed. 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.…

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

Taschenbuch. Etat : 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. Etat : 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. …

Etat : New.

Etat : New. 2024th edition NO-PA16APR2015-KAP.

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

Hardback. Etat : New. 2024 ed. 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.…

Etat : new. Questo è un articolo print on demand.

Etat : New. Print on Demand.