LEADER 02766nam 2200409 450 001 9910645945103321 005 20230627142950.0 024 7 $a10.1515/9783110785968 035 $a(CKB)5860000000285474 035 $a(NjHacI)995860000000285474 035 $a(EXLCZ)995860000000285474 100 $a20230627d2022 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning under Resource Constraints $eDiscovery in Physics /$fEdited by Katharina Morik, Wolfgang Rhode 210 1$aBerlin :$cDe Gruyter,$d2022. 210 4$dİ2022 215 $a1 online resource (ix, 347 pages) 225 1 $aDe Gruyter STEM ;$vVolume 2 311 $a3-11-078613-3 320 $aIncludes bibliographical references and index. 330 $aMachine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning. 410 0$aDe Gruyter STEM ;$vVolume 2. 606 $aArtificial intelligence 615 0$aArtificial intelligence. 676 $a006.3 702 $aMorik$b Katharina 702 $aRhode$b Wolfgang 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910645945103321 996 $aMachine Learning under Resource Constraints$93011774 997 $aUNINA