• Epistemic Uncertainty Quantification in Deep Learning by the Delta Method 

      Nilsen, Geir Kjetil (Doctoral thesis, 2022-05-16)
      This thesis explores the Delta method and its application to deep learning image classification. The Delta method is a classical procedure for quantifying uncertainty in statistical models, but its direct application to ...
    • Epistemic uncertainty quantification in deep learning classification by the Delta method 

      Nilsen, Geir Kjetil; Munthe-Kaas, Antonella Zanna; Skaug, Hans Julius; Brun, Morten (Journal article; Peer reviewed, 2022)
      The Delta method is a classical procedure for quantifying epistemic uncertainty in statistical models, but its direct application to deep neural networks is prevented by the large number of parameters . We propose a low ...
    • Recursive Time-Frequency Reassignment 

      Nilsen, Geir Kjetil (Master thesis, 2008-11-18)
      A fast algorithm for producing time-frequency representations (TFRs) is proposed. The resulting TFRs have optional time-frequency resolution up to optimality. The algorithm is further extended with a method known as ...