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Statistical Analysis, Machine Learning and Image Analysis - SAMBA

The SAMBA department has comprehensive theoretical and practical knowledge in the fields of statistics, machine learning and image analysis. We are one of Europe's largest and most competent groups within applied statistics and statistical-matematical modelling. We cover a broad spectrum of methods and are a world leader in some of these areas. The appropriate choice of method for the various problems is thus one of our strengths. Many calculations involve uncertainty and the accurate calculation of this quantity is an important speciality.

Research areas

Last 5 scientific articles

    Lison, Pierre; Mavroeidis, Vasileios. Neural Reputation Models learned from Passive DNS data. In: IEEE Big Data 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention 2017. (ISBN 978-1-5386-2714-3). 2017. Full-text 

    Lison, Pierre; Mavroeidis, Vasileios. Automatic Detection of Malware-Generated Domains with Recurrent Neural Models. Norsk Informasjonssikkerhetskonferanse (NISK) (ISSN 1893-6563). 2017. Full-text 

    Kampffmeyer, Michael C.; Løkse, Sigurd; Bianchi, Filippo Maria; Livi, Lorenzo; Salberg, Arnt Børre; Jenssen, Robert. Deep divergence-based clustering. In: 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). (ISBN 978-1-5090-6341-3). 2017.

    Kampffmeyer, Michael C.; Salberg, Arnt Børre; Jenssen, Robert. Urban land cover classification with missing data using deep convolutional neural networks. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). (ISBN 978-1-5090-4951-6). pp 5161-5164. doi: 10.1109/IGARSS.2017.8128164. 2017. Full-text 

    Kwinta, Przemko; Bokiniec, Renata; Bik-Multanowski, Miroslaw; Günther, Clara-Cecilie; Grabowska, Agnieszka; Grabowska, A.; Ksiazek, Teofila; Madetko-Talowska, Anna; Szewczyk, Katarzyna; Szwarc-Duma, Monika; Borszewska-Kornacka, Maria K.; Baumbusch, Lars Oliver; Revhaug, Cecilie; Saugstad, Ola Didrik; Pietrzyk, Jacek J.. Comparison of whole genome expression profile between preterm and full-term newborns. Ginekologia Polska (ISSN 0017-0011). 88(8) pp 434-441. doi: 10.5603/GP.a2017.0080. 2017.

Publications in 2018, 2017, 2016, 2015, 2014, earlier years
Postal address:
Norsk Regnesentral/
Norwegian Computing Center
P.O. Box 114 Blindern
NO-0314 Oslo
Visit address:
Norsk Regnesentral
Gaustadalleen 23a
Kristen Nygaards hus
NO-0373 Oslo.
(+47) 22 85 25 00
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Postal address: Norsk Regnesentral/Norwegian Computing Center, P.O. Box 114 Blindern, NO-0314 Oslo, Norway
Visit address: Norsk Regnesentral, Gaustadalleen 23a, Kristen Nygaards hus, NO-0373 Oslo.
Phone: (+47) 22 85 25 00
AddressHow to get to NR