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  • R2030: Digitaliseringsvennlig regelverk. Metoder for regelverksforenkling, med eksempler fra bruksforskriften 

    Stolpe, Audun; Kristoffersen, Thor O.; Østvold, Bjarte M. (NR-notat;, Research report, 2023)
    Digitalisering av regelverk gir effektiviseringsgevinster gjennom utvikling av digitale tjenester. For at et regelverk skal kunne digitaliseres effektivt, er det viktig å sørge for at reglene er så enkle som mulige og at ...
  • Automatic identification of chemical moieties 

    Lederer, Jonas; Gastegger, Michael; Schütt, Kristof T.; Kampffmeyer, Michael Christian; Müller, Klaus-Robert; Unke, Oliver T. (Journal article; Peer reviewed, 2023)
    In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, ...
  • Some recent trends in embeddings of time series and dynamic networks 

    Tjøstheim, Dag Bjarne; Jullum, Martin; Løland, Anders (Journal article; Peer reviewed, 2023)
    We give a review of some recent developments in embeddings of time series and dynamic networks. We start out with traditional principal components and then look at extensions to dynamic factor models for time series. Unlike ...
  • View it like a radiologist: Shifted windows for deep learning augmentation of CT images 

    Østmo, Eirik Agnalt; Wickstrøm, Kristoffer; Radiya, Keyur; Kampffmeyer, Michael; Jenssen, Robert (Journal article; Peer reviewed, 2023)
    Deep learning has the potential to revolutionize medical practice by automating and performing important tasks like detecting and delineating the size and locations of cancers in medical images. However, most deep learning ...
  • Statistical Embedding: Beyond Principal Components 

    Tjøstheim, Dag Bjarne; Jullum, Martin; Løland, Anders (Journal article; Peer reviewed, 2023)
    There has been an intense recent activity in embedding of very high-dimensional and nonlinear data structures, much of it in the data science and machine learning literature. We survey this activity in four parts. In the ...

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