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Machine Learning under Resource Constraints. Discovery in Physics / / ed. by Katharina Morik, Wolfgang Rhode



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Titolo: Machine Learning under Resource Constraints. Discovery in Physics / / ed. by Katharina Morik, Wolfgang Rhode Visualizza cluster
Pubblicazione: Berlin ; ; Boston : , : De Gruyter, , [2022]
©2023
Edizione: 1st ed.
Descrizione fisica: 1 online resource (XIV, 349 p.)
Disciplina: 006.31
Soggetto topico: SCIENCE / Chemistry / General
Soggetto non controllato: Artificial Intelligence
Big Data and Machine Learning
Cyber-physical systems
Data mining for Ubiquitous System Software
Embedded Systems and Machine Learning
Highly Distributed Data
ML on Small devices
Machine learning for knowledge discovery
Machine learning in high-energy physics
Resource-Aware Machine Learning
Resource-Constrained Data Analysis
Persona (resp. second.): MorikKatharina
RhodeWolfgang
Nota di contenuto: Frontmatter -- Contents -- 1 Introduction -- 2 Challenges in Particle and Astroparticle Physics -- 3 Key Concepts in Machine Learning and Data Analysis -- 4 Data Acquisition and Data Structure -- 5 Monte Carlo Simulations -- 6 Data Storage and Access -- 7 Monitoring and Feature Extraction -- 8 Event Property Estimation and Signal Background Separation -- 9 Deep Learning Applications -- 10 Inverse Problems -- Bibliography -- Index -- List of Contributors
Sommario/riassunto: 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.
Titolo autorizzato: Machine Learning under Resource Constraints  Visualizza cluster
ISBN: 3-11-078596-X
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 996503570203316
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Serie: De Gruyter STEM Series