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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; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre. Multi-View Self-Constructing Graph Convolutional Networks With Adaptive Class Weighting Loss for Semantic Segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2020. (ISBN 978-1-7281-9360-1). pp 199-205. doi: 10.1109/CVPRW50498.2020.00030. 2020.

    Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert. Deep generative models for reject inference in credit scoring. Knowledge-Based Systems (ISSN 0950-7051). 196 doi: 10.1016/j.knosys.2020.105758. 2020.

    Ordonez, Alba; Eikvil, Line; Salberg, Arnt-Børre; Harbitz, Alf; Murray, Sean Meling; Kampffmeyer, Michael. Explaining decisions of deep neural networks used for fish age prediction. PLOS ONE (ISSN 1932-6203). 15(6) doi: 10.1371/journal.pone.0235013. 2020.

    Lison, Pierre; Barnes, Jeremy; Hubin, Aliaksandr; Touileb, Samia. Named Entity Recognition without Labelled Data: A Weak Supervision Approach. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. (ISBN 978-1-952148-25-5). pp 1518-1533. 2020.

    Hubin, Aliaksandr; Storvik, Geir Olve; Frommlet, Florian. Rejoinder for the discussion of the paper "A Novel Algorithmic Approach to Bayesian Logic Regression". Bayesian Analysis (ISSN 1936-0975). 15(1) pp 312-333. 2020.

Publications in 2020, 2019, 2018, 2017, 2016, 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