Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Mathieu Sinn"'
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031168147
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::883af07e209a7e94b2628e8de49e57c8
https://doi.org/10.1007/978-3-031-16815-4_21
https://doi.org/10.1007/978-3-031-16815-4_21
Publikováno v:
Computer Security – ESORICS 2022 ISBN: 9783031171420
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::281d40b651642010f1cea2b261acbd16
https://doi.org/10.1007/978-3-031-17143-7_41
https://doi.org/10.1007/978-3-031-17143-7_41
Autor:
Nathalie Baracaldo, Bashar Altakrouri, Matthias Buchinger, Ali Anwar, Mahdi Sellami, Mark Purcell, Peter Kuhn, Heiko Ludwig, Dian Balta, Mathieu Sinn, Ulrich Schöpp
Publikováno v:
Electronic Participation ISBN: 9783030828233
ePart
ePart
Machine learning offers promising capabilities to improve administrative procedures. At the same time, adequate training of models using traditional learning techniques requires the collection and storage of enough training data in a central place. U
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::729a48f5971245d0960eb8e1b926c399
https://doi.org/10.1007/978-3-030-82824-0_10
https://doi.org/10.1007/978-3-030-82824-0_10
This deliverable (D3.2 "Architecture Design – Final Version") is a document describing the architecture for the MUSKETEER centralized server platform. It is the culmination of task T3.1 and builds upon the initial architecture document D3.1, provid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0438c485a5e794eb91b6c452a67fb9b6
A demonstration and report of a first prototype of the MUSKETEER platform, demonstrating the end-to-end execution of data sharing and federated machine learning for synthetic data and at least one use case, supporting privacy operating modes POM1-POM
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03cbd05e3a118fd9ce427461e8014f1f
Publikováno v:
IEEE Transactions on Signal Processing. 65:1352-1365
Additive models form a widely popular class of regression models that represent the relation between covariates and response variables as the sum of low-dimensional transfer functions . Besides flexibility and accuracy, a key benefit of these models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a13fe6aff2bc0667b7334be6a1850f7b
https://doi.org/10.1109/tsp.2016.2634546
https://doi.org/10.1109/tsp.2016.2634546
This deliverable (D3.1 "Architecture Design") is a document describing the initial version ofthe MUSKETEER platform architecture. It addresses the previously delivered technical requirementsand key performance indicators, takes into account legal and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::676cc6112971ff56a329b33256b4c378
Autor:
Carlos Alzate, Anna Monreale, Haytham Assem, Albert Bifet, Teodora Sandra Buda, Bora Caglayan, Brett Drury, Eva García-Martín, Ricard Gavaldà, Irena Koprinska, Stefan Kramer, Niklas Lavesson, Michael Madden, Ian Molloy, Maria-Irina Nicolae, Mathieu Sinn
This book constitutes revised selected papers from the workshops Nemesis, UrbReas, SoGood, IWAISe, and Green Data Mining, held at the 18th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, in Dublin, Irelan
Autor:
Bei Chen, Robert Gormally, Francesco Fusco, Seshu Tirupathi, Mathieu Sinn, Bradley J. Eck, Mark Purcell
Publikováno v:
2018 IEEE International Conference on Data Mining Workshops (ICDMW)
ICDM Workshops
ICDM Workshops
We demonstrate Castor, a cloud-based system for contextual IoT time series data and model management at scale. Castor is designed to assist Data Scientists in (a) exploring and retrieving all relevant time series and contextual information that is re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc505b44a7b5f0b055cadd4d56adc668
http://arxiv.org/abs/1811.08566
http://arxiv.org/abs/1811.08566
Autor:
Mathieu Sinn, Magnus Mossberg
Publikováno v:
ICASSP
Zero crossing data contain information of a process in compact form and is therefore of interest in wireless sensor networks where only reduced amounts of data can be transmitted. When analyzing the properties of certain algorithms using zero crossin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6db68ebbe3cbdd4570495a34dad2b364
https://doi.org/10.1109/icassp.2017.7952965
https://doi.org/10.1109/icassp.2017.7952965