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SAMBA

SAMBA

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

    Liu, Qinghui; Salberg, Arnt Børre; Jenssen, Robert. A COMPARISON OF DEEP LEARNING ARCHITECTURES FOR SEMANTIC MAPPING OF VERY HIGH RESOLUTION IMAGES. IEEE International Geoscience and Remote Sensing Symposium proceedings (ISSN 2153-6996). 2018.

    Aldrin, Magne Tommy; Jansen, Peder A; Stryhn, Henrik. A partly stage-structured model for the abundance of salmon lice in salmonid farms. Epidemics (ISSN 1755-4365). pp 1-14. doi: 10.1016/j.epidem.2018.08.001. 2018.

    Tvete, Ingunn Fride; Bjørner, Trine; Skomedal, Tor. Mental Health and Disability Pension Onset Changes in Consumption of Antianxiety and Hypnotic Drugs. Health Services Research and Managerial Epidemiology (ISSN 2333-3928). 5 doi: 10.1177/2333392818792683. 2018.

    Thorarinsdottir, Thordis Linda; Hellton, Kristoffer Herland; Steinbakk, Gunnhildur Högnadóttir; Schlichting, Lena; Engeland, Kolbjørn. Bayesian regional flood frequency analysis for large catchments. Water Resources Research (ISSN 0043-1397). 54(9) pp 6929-6947. doi: 10.1029/2017WR022460. 2018.

    Kampffmeyer, Michael C.; Salberg, Arnt Børre; Jenssen, Robert. Urban land cover classification with missing data modalities using deep convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (ISSN 1939-1404). 11(6) pp 1758-1768. doi: 10.1109/JSTARS.2018.2834961. 2018.

Publications in 2018, 2017, 2016, 2015, 2014, earlier years
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
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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