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123. Multi-task learning in programmatic advertisingDOMEN VREŠ, 2023, magistrsko delo Ključne besede: artificial intelligence, machine learning, multi-task learning, real-time bidding, viewability prediction, click-through rate prediction, conversion rate prediction, deep & cross network, cross-stitch layer, soft attention, uncertainty based loss weighing Celotno besedilo (datoteka, 1,39 MB) |
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126. A hybrid modeling strategy for training data generation in machine learning-based structural health monitoringTim Vrtač, Domen Ocepek, Martin Česnik, Gregor Čepon, Miha Boltežar, 2024, izvirni znanstveni članek Ključne besede: structural health monitoring, joint-damage identification, Frequency Based Substructuring, machine learning, training set generation Celotno besedilo (datoteka, 3,53 MB) Gradivo ima več datotek! Več... |
127. Deep learning of tissue-specific gene expression from DNA sequencesUroš Polanc, 2023, magistrsko delo Ključne besede: bioinformatics, convolutional neural network, DNA, DNABERT, gene expression, machine learning, sequence motifs, mRNA, predictive models, regulatory mechanisms, tissue-specific gene expression, tissue-specificity Celotno besedilo (datoteka, 8,50 MB) |
128. Identification of electroporation sites in the complex lipid organization of the plasma membraneLea Rems, Xinru Tang, Fangwei Zhao, Sergio Pérez-Conesa, Ilaria Testa, Lucie Delemotte, 2022, izvirni znanstveni članek Ključne besede: plasma membrane, electric field, pores, molecular dynamics simulation, machine learning, Bayesian survival analysis Celotno besedilo (datoteka, 3,08 MB) Gradivo ima več datotek! Več... |
129. Application of machine learning models for estimating the material parameters for multiaxial fatigue strength calculationMarko Nagode, Jan Papuga, Simon Oman, 2023, izvirni znanstveni članek Ključne besede: estimation of material parameters, fatigue strength, multiaxial fatigue analysis, machine learning, random forest Celotno besedilo (datoteka, 1,01 MB) Gradivo ima več datotek! Več... |
130. Search for type-II seesaw mechanism processes with same charge leptons in the final states with the ATLAS detectorBlaž Leban, 2023, doktorska disertacija Ključne besede: ATLAS, CERN, LHC Run 2, left-right symmetry, type-II seesaw, Zee-Babu model, machine learning, boosted decision trees, neural networks, same-charge leptons, doubly charged Higgs, lepton flavour violation Celotno besedilo (datoteka, 9,17 MB) |