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RESEARCH
High performance computing for machine learning and multi-task learning
Research of scaling up and optimization of machine learning methods related to big data. The methods include standard classification methods like SVM and also novel data integration approaches based on Bayesian matrix factorization. The target application is drug-related datasets with millions of compounds and 100 of millions of activities where having highly scalable methods is crucial.
High performance computing for machine learning and multi-task learning
Research of scaling up and optimization of machine learning methods related to big data. The methods include standard classification methods like SVM and also novel data integration approaches based on Bayesian matrix factorization. The target application is drug-related datasets with millions of compounds and 100 of millions of activities where having highly scalable methods is crucial.
研究兴趣
论文共 99 篇作者统计合作学者相似作者
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medRxiv : the preprint server for health sciences (2024)
Artificial Intelligence in the Life Sciences (2023): 100070-100070
Wouter Heyndrickx,Lewis Mervin, Tobias Morawietz,Noe Sturm,Lukas Friedrich,Adam Zalewski,Anastasia Pentina,Lina Humbeck,Martijn Oldenhof, Ritsuya Niwayama,Peter Schmidtke,Nikolas Fechner,
JOURNAL OF CHEMICAL INFORMATION AND MODELINGno. 7 (2023): 2331-2344
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE (LOD 2021), PT II (2022): 198-212
Martijn Oldenhof,Gergely Ács,Balázs Pejó,Ansgar Schuffenhauer,Nicholas Holway,Noé Sturm, Arne Dieckmann, Oliver Fortmeier, Eric Boniface, Clément Mayer,Arnaud Gohier,Peter Schmidtke,
arxiv(2022)
BNAIC/BENELEARN (2021): 46-65
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