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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

    Kampffmeyer, Michael C.; Løkse, Sigurd; Bianchi, Filippo Maria; Livi, Lorenzo; Salberg, Arnt Børre; Jenssen, Robert. Deep divergence-based approach to clustering. Neural Networks (ISSN 0893-6080). 113 pp 91-101. doi: 10.1016/j.neunet.2019.01.015. 2019.

    Revhaug, Cecilie; Zasada, Magdalena; Rognlien, Anne Gro Wesenberg; Günther, Clara-Cecilie; Grabowska, Agnieszka; Książek, Teofila; Madetko-Talowska, Anna; Szewczyk, Katarzyna; Bik-Multanowski, Miroslaw; Kwinta, Przemko; Pietrzyk, Jacek Józef; Baumbusch, Lars Oliver; Saugstad, Ola Didrik. Pulmonary vascular disease is evident in gene regulation of experimental bronchopulmonary dysplasia. Journal of Maternal-Fetal & Neonatal Medicine (ISSN 1476-7058). pp 1-10. doi: 10.1080/14767058.2018.1541081. 2019.

    Hubin, Aliaksandr; Storvik, Geir Olve; Frommlet, Florian. A Novel Algorithmic Approach to Bayesian Logic Regression. Bayesian Analysis (ISSN 1936-0975). doi: 10.1214/18-BA1141. 2018.

    Liu, Qinghui; Salberg, Arnt Børre; Jenssen, Robert. A Comparison of Deep Learning Architectures for Semantic Mapping of Very High Resolution Images. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. (ISBN 978-1-5386-7150-4). pp 6943-6946. doi: 10.1109/IGARSS.2018.8518533. 2018. Full-text 

    Kermit, Martin Andreas; Hamar, Jarle Bauck; Trier, Øivind Due. Towards a national infrastructure for semi-automatic mapping of cultural heritage in Norway. In: Oceans of data. Proceedings of the 44th Annual Conference on Computer Applications and Quantitative Methods in Archaeology. (ISBN 9781784917302). pp 159-172. 2018. Full-text 

Publications in 2019, 2018, 2017, 2016, 2015, 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