Zobrazeno 1 - 10
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pro vyhledávání: '"Liu, Peini"'
Machine Learning (ML) models struggle with data that changes over time or across domains due to factors such as noise, occlusion, illumination, or frequency, unlike humans who can learn from such non independent and identically distributed data. Cons
Externí odkaz:
http://arxiv.org/abs/2306.11955
Autor:
Liu, Peini, Guitart, Jordi
Containerization technology offers lightweight OS-level virtualization, and enables portability, reproducibility, and flexibility by packing applications with low performance overhead and low effort to maintain and scale them. Moreover, container orc
Externí odkaz:
http://arxiv.org/abs/2211.11487
Autor:
Bravo-Rocca, Gusseppe, Liu, Peini, Guitart, Jordi, Dholakia, Ajay, Ellison, David, Hodak, Miroslav
Increasing a ML model accuracy is not enough, we must also increase its trustworthiness. This is an important step for building resilient AI systems for safety-critical applications such as automotive, finance, and healthcare. For that purpose, we pr
Externí odkaz:
http://arxiv.org/abs/2204.14255
Autor:
Bravo-Rocca, Gusseppe, Liu, Peini, Guitart, Jordi, Dholakia, Ajay, Ellison, David, Falkanger, Jeffrey, Hodak, Miroslav
Machine Learning (ML) is more than just training models, the whole workflow must be considered. Once deployed, a ML model needs to be watched and constantly supervised and debugged to guarantee its validity and robustness in unexpected situations. De
Externí odkaz:
http://arxiv.org/abs/2111.03003
Autor:
Bravo-Rocca, Gusseppe, Liu, Peini, Guitart, Jordi, Dholakia, Ajay, Ellison, David, Falkanger, Jeffrey, Hodak, Miroslav
Publikováno v:
In Expert Systems With Applications 15 September 2022 202
Autor:
Liu, Peini1,2 (AUTHOR) peini.liu@bsc.es, Guitart, Jordi1,2 (AUTHOR)
Publikováno v:
Journal of Supercomputing. Jun2021, Vol. 77 Issue 6, p6273-6312. 40p.
Autor:
Liu, Peini|||0000-0003-0058-8732, Bravo Rocca, Gusseppe, Guitart Fernández, Jordi|||0000-0003-0751-3100, Dholakia, Ajay, Ellison, David, Hodak, Miroslav
Publikováno v:
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Universitat Politècnica de Catalunya (UPC)
Machine Learning (ML) projects are currently heavily based on workflows composed of some reproducible steps and executed as containerized pipelines to build or deploy ML models efficiently because of the flexibility, portability, and fast delivery th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::87aea3d57d26f4de7c123da60f2640e3
https://hdl.handle.net/2117/371292
https://hdl.handle.net/2117/371292
Akademický článek
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Publikováno v:
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Universitat Politècnica de Catalunya (UPC)
Scientific computation problems have been faced with the need to analyze increasing amounts of data as part of their application workflows, and the science-based model is being combined with big data and machine learning models to solve complex probl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::85310ac14cc2cbfb788d052122c441d4
https://hdl.handle.net/2117/346332
https://hdl.handle.net/2117/346332
Autor:
Liu, Peini, Guitart, Jordi
Publikováno v:
Cluster Computing; Apr2022, Vol. 25 Issue 2, p847-868, 22p