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  • Data assimilation and statistical post-processing for numerical air quality predictions 

    Steinbakk, Gunnhildur Högnadóttir; Thorarinsdottir, Thordis Linda; Lahoz, William A.; Walker, Sam-Erik (NR-notat;, Research report, 2014)
    This is a joint report based on work by NILU and NR on the use of data assimilation and statistical post-processing tools to improve the air quality prediction in the context of the Bedre Byluft programme. The objective ...
  • Novel and Efficient Privacy-Preserving Continuous Authentication 

    Baig, Ahmed Fraz; Eskeland, Sigurd; Yang, Bian (Journal article; Peer reviewed, 2024)
    Continuous authentication enhances security by re-verifying a user’s validity during the active session. It utilizes data about users’ behavioral actions and contextual information to authenticate them continuously. Such ...
  • On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering 

    Trosten, Daniel Johansen; Løkse, Sigurd Eivindson; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2023)
    Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-based methods for deep MVC, potentially ...
  • A new framework for semi-Markovian parametric multi-state models with interval censoring 

    Aastveit, Marthe Elisabeth; Cunen, Celine Marie Løken; Hjort, Nils Lid (Journal article; Peer reviewed, 2023)
    There are few computational and methodological tools available for the analysis of general multi-state models with interval censoring. Here, we propose a general framework for parametric inference with interval censored ...
  • Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings 

    Trosten, Daniel Johansen; Chakraborty, Rwiddhi; Løkse, Sigurd Eivindson; Wickstrøm, Kristoffer; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2023)
    Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where ...

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