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Ergebnis der Suche nach: "Machine Learning"
im Bestand: Gesamter Bestand

36601 - 36610 von 37157
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Artikel 36601 Unifying logic rules and machine learning for entity enhancing
Enthalten in Science China / Information sciences Bd. 63, 8.6.2020, Nr. 7, date:7.2020: 1-19
Online Ressource
Artikel 36602 UNIK (Urologic Non-Neoplastic Investigation of Kidneys): a machine learning approach to decode benign lesion
Enthalten in World journal of urology Bd. 43, 4.6.2025, Nr. 1, date:12.2025: 1-7
Online Ressource
Artikel 36603 Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: a Multiple-Case Study from Greece
Enthalten in Water resources management Bd. 32, 29.11.2018, Nr. 15, date:12.2018: 5207-5239
Online Ressource
Artikel 36604 Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory
Enthalten in Nature Communications Bd. 15, 2.1.2024, Nr. 1, date:12.2024: 1-10
Online Ressource
Artikel 36605 Universal machine learning for the response of atomistic systems to external fields
Enthalten in Nature Communications Bd. 14, 12.10.2023, Nr. 1, date:12.2023: 1-11
Online Ressource
Artikel 36606 Universal machine learning interatomic potentials are ready for phonons
Enthalten in npj computational materials Bd. 11, 12.6.2025, Nr. 1, date:12.2025: 1-8
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Artikel 36607 Universality class of machine learning for critical phenomena
Enthalten in Science China / Physics, mechanics & astronomy Bd. 66, 17.11.2023, Nr. 12, date:12.2023: 1-8
Online Ressource
Artikel 36608 Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights
Enthalten in Scientific reports Bd. 14, 20.1.2024, Nr. 1, date:12.2024: 1-15
Online Ressource
Artikel 36609 Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning
Enthalten in Scientific reports Bd. 9, 26.2.2019, Nr. 1, date:12.2019: 1-10
Online Ressource
Artikel 36610 Unlocking phonon properties of a large and diverse set of cubic crystals by indirect bottom-up machine learning approach
Enthalten in Communications materials Bd. 4, 15.8.2023, Nr. 1, date:12.2023: 1-11
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