LEADER 04058nam 22006135 450 001 996503570203316 005 20230103011142.0 010 $a3-11-078596-X 024 7 $a10.1515/9783110785968 035 $a(CKB)5580000000492179 035 $a(DE-B1597)617926 035 $a(DE-B1597)9783110785968 035 $a(MiAaPQ)EBC7156399 035 $a(Au-PeEL)EBL7156399 035 $a(OCoLC)1356978797 035 $a(EXLCZ)995580000000492179 100 $a20230103h20222023 fg 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning under Resource Constraints. $iDiscovery in Physics /$fed. by Katharina Morik, Wolfgang Rhode 205 $a1st ed. 210 1$aBerlin ;$aBoston : $cDe Gruyter, $d[2022] 210 4$dİ2023 215 $a1 online resource (XIV, 349 p.) 225 0 $aDe Gruyter STEM ;$vVolume 2 311 $a3-11-078595-1 327 $tFrontmatter -- $tContents -- $t1 Introduction -- $t2 Challenges in Particle and Astroparticle Physics -- $t3 Key Concepts in Machine Learning and Data Analysis -- $t4 Data Acquisition and Data Structure -- $t5 Monte Carlo Simulations -- $t6 Data Storage and Access -- $t7 Monitoring and Feature Extraction -- $t8 Event Property Estimation and Signal Background Separation -- $t9 Deep Learning Applications -- $t10 Inverse Problems -- $tBibliography -- $tIndex -- $tList of Contributors 330 $aMachine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning. 410 3$aDe Gruyter STEM Series 606 $aSCIENCE / Chemistry / General$2bisacsh 610 $aArtificial Intelligence. 610 $aBig Data and Machine Learning. 610 $aCyber-physical systems. 610 $aData mining for Ubiquitous System Software. 610 $aEmbedded Systems and Machine Learning. 610 $aHighly Distributed Data. 610 $aML on Small devices. 610 $aMachine learning for knowledge discovery. 610 $aMachine learning in high-energy physics. 610 $aResource-Aware Machine Learning. 610 $aResource-Constrained Data Analysis. 615 7$aSCIENCE / Chemistry / General. 676 $a006.31 702 $aMorik$b Katharina, $4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRhode$b Wolfgang, $4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bDE-B1597 801 1$bDE-B1597 906 $aBOOK 912 $a996503570203316 996 $aMachine Learning under Resource Constraints$93011774 997 $aUNISA