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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| Formato: | Libro |
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De Gruyter
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3501234&site=ehost-live header: @attributes: shortDbName: nlebk uiTerm: 3501234 longDbName: eBook Collection (EBSCOhost) uiTag: AN controlInfo: bkinfo: btl: Machine Learning Under Resource Constraints - Discovery in Physics aug: au: Katharina Morik Wolfgang Rhode sertl: Machine Learning Under Resource Constraints isbn: 9783110785951 9783110785968 9783110786132 imageinfo: pubinfo: dt: @attributes: year: 2023 month: 01 day: 01 dtAvail: @attributes: year: 2023 month: 02 day: 10 vid: 00002 pub: De Gruyter pubContract: De Gruyter place: Berlin price: 0.01 limitsGroup: maxCheckoutDays: 1500 pda: N printPagesOffline: 100 printPagesOnline: 100 previewPages: 10000 prePubGroup: dewey: @attributes: class: 539.720285631 item: 539 .720285631 lc: @attributes: class: QC783.3 item: QC 783 .3 artinfo: ui: 3501234 1356978797 formats: fmt: – @attributes: type: EB doid: NL$3501234$PDF caption: PDF download: Y – @attributes: type: EK doid: NL$3501234$EPUB caption: EPUB download: Y tig: atl: Machine Learning Under Resource Constraints - Discovery in Physics ptl: Machine Learning Under Resource Constraints - Discovery in Physics aug: au: Katharina Morik Wolfgang Rhode su: Machine learning Particles (Nuclear physics)--Data processing sug: subj: COMPUTERS / Information Technology COMPUTERS / Data Science / General COMPUTERS / Data Science / Data Analytics COMPUTERS / Programming / Algorithms Machine learning Particles (Nuclear physics)--Data processing ab: 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. pubtype: eBook doctype: Book ougenre: Book language: English copyright: @attributes: flag: N copyrightText: holdings: @attributes: islocal: N |
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