Author : Ji Liu
Publisher :
Page : pages
File Size : 12,52 MB
Release : 2020
Category : Electronic books
ISBN : 9781680837018
This monograph provides students and researchers the groundwork for developing faster and better research results in this dynamic area of research.
Author : Ji Liu
Publisher : Now Publishers
Page : 114 pages
File Size : 45,69 MB
Release : 2020-06-17
Category : Computers
ISBN : 9781680837001
Scalable and efficient distributed learning is one of the main driving forces behind the recent rapid advancement of machine learning and artificial intelligence. One prominent feature of this development is that recent progress has been made by researchers in two communities: (1) the system community such as database, data management, and distributed systems, and (2) the machine learning and mathematical optimization community. The interaction and knowledge sharing between these two communities has led to the rapid development of new distributed learning systems and theory. This monograph provides a brief introduction to three distributed learning techniques that have recently been developed: lossy communication compression, asynchronous communication, and decentralized communication. These have significant impact on the work in both the system and machine learning and mathematical optimization communities but to fully realize the potential, it is essential they understand the whole picture. This monograph provides the bridge between the two communities. The simplified introduction to the essential aspects of each community enables researchers to gain insights into the factors influencing both. The monograph provides students and researchers the groundwork for developing faster and better research results in this dynamic area of research.
Author : Guanghui Lan
Publisher : Springer Nature
Page : 591 pages
File Size : 26,73 MB
Release : 2020-05-15
Category : Mathematics
ISBN : 3030395685
This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
Author : Stephen Boyd
Publisher : Now Publishers Inc
Page : 138 pages
File Size : 38,87 MB
Release : 2011
Category : Computers
ISBN : 160198460X
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Author : Wan Fokkink
Publisher : MIT Press
Page : 242 pages
File Size : 42,36 MB
Release : 2013-12-06
Category : Computers
ISBN : 0262318954
A comprehensive guide to distributed algorithms that emphasizes examples and exercises rather than mathematical argumentation. This book offers students and researchers a guide to distributed algorithms that emphasizes examples and exercises rather than the intricacies of mathematical models. It avoids mathematical argumentation, often a stumbling block for students, teaching algorithmic thought rather than proofs and logic. This approach allows the student to learn a large number of algorithms within a relatively short span of time. Algorithms are explained through brief, informal descriptions, illuminating examples, and practical exercises. The examples and exercises allow readers to understand algorithms intuitively and from different perspectives. Proof sketches, arguing the correctness of an algorithm or explaining the idea behind fundamental results, are also included. An appendix offers pseudocode descriptions of many algorithms. Distributed algorithms are performed by a collection of computers that send messages to each other or by multiple software threads that use the same shared memory. The algorithms presented in the book are for the most part “classics,” selected because they shed light on the algorithmic design of distributed systems or on key issues in distributed computing and concurrent programming. Distributed Algorithms can be used in courses for upper-level undergraduates or graduate students in computer science, or as a reference for researchers in the field.
Author : Phillip E. Pace
Publisher : Artech House
Page : 920 pages
File Size : 44,22 MB
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 1630816981
This book provides a comprehensive resource and thorough treatment in the latest development of Digital RF Memory (DRFM) technology and their key role in maintaining dominance over the electromagnetic spectrum. Part I discusses the use of advanced technology to design transceivers for spectrum sensing using unmanned systems to dominate the electromagnetic spectrum. Part II uses artificial intelligence and machine learning to enable modern spectrum sensing and detection signal processing for electronic support and electronic attack. Another key contribution is examination of counter-DRFM techniques. DRFM and transceiver design details and examples are provided along with the MATLAB software allowing the reader to construct their own embedded DRFM transceivers for unmanned systems. It examines the design trade-offs in developing multiple, structured, false target synthesis DRFM architectures and aids in developing counter-DRFM techniques and distinguish false target from real ones. Written by an expert in the field, and including MATLAB™ design software, this is the only comprehensive book written on the subject of DRFM.
Author : Qiang Yang
Publisher : Springer Nature
Page : 291 pages
File Size : 32,99 MB
Release : 2020-11-25
Category : Computers
ISBN : 3030630765
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Author : Pontus Giselsson
Publisher : Springer
Page : 416 pages
File Size : 11,32 MB
Release : 2018-11-11
Category : Mathematics
ISBN : 3319974785
This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade emerged to become very important. As well as specific coverage of this active research field, the book serves as a powerful source of information for practitioners as well as theoreticians. Large-Scale and Distributed Optimization is a unique combination of contributions from leading experts in the field, who were speakers at the LCCC Focus Period on Large-Scale and Distributed Optimization, held in Lund, 14th–16th June 2017. A source of information and innovative ideas for current and future research, this book will appeal to researchers, academics, and students who are interested in large-scale optimization.
Author : Qingguo Lü
Publisher : Springer Nature
Page : 282 pages
File Size : 24,51 MB
Release : 2023-02-08
Category : Computers
ISBN : 9811985596
This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.
Author : Andrew S Tanenbaum
Publisher : Maarten Van Steen
Page : 0 pages
File Size : 17,34 MB
Release : 2023-01-08
Category :
ISBN : 9789081540636
This is the fourth edition of "Distributed Systems." We have stayed close to the setup of the third edition, including examples of (part of) existing distributed systems close to where general principles are discussed. For example, we have included material on blockchain systems, and discuss their various components throughout the book. We have, again, used special boxed sections for material that can be skipped at first reading. The text has been thoroughly reviewed, revised, and updated. In particular, all the Python code has been updated to Python3, while at the same time the channel package has been almost completely revised and simplified. Additional material, including coding examples, figures, and slides, are available at www.distributed-systems.net.
