Machine Learning under Resource Constraints. Volume 2, Machine Learning under Resource Constraints - Discovery in Physics

Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data a...

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Bibliographic Details
Corporate Author:	Walter de Gruyter & Co
Other Authors:	Morik, Katharina (Editor), Rhode, Wolfgang (Editor)
Format:	eBook
Language:	English
Language Notes:	In English.
Published:	Berlin Boston De Gruyter, [2022]
Series:	De Gruyter STEM
Subjects:	COM018000 COMPUTERS / Data Processing. COMPUTERS / Database Management / Data Mining. COMPUTERS / Information Technology. COMPUTERS / Programming / Algorithms. Künstliche Intelligenz Maschinelles Lernen Eingebettete Systeme Big Data Resource-Constrained Data Analysis Resource-Aware Machine Learning Embedded Systems and Machine Learning Big Data and Machine Learning Artificial Intelligence Highly Distributed Data ML on Small devices Data mining for Ubiquitous System Software Cyber-physical systems Machine learning in high-energy physics Machine learning for knowledge discovery Electronic books.
Online Access:	Connect to the full text of this electronic book

Description
Summary:	Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.
Physical Description:	1 online resource (XIV, 349 pages).
ISBN:	9783110785968 311078596X