• Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy 

      Wickstrøm, Kristoffer Knutsen; Løkse, Sigurd Eivindson; Kampffmeyer, Michael Christian; Yu, Shujian; Príncipe, José C.; Jenssen, Robert (Journal article; Peer reviewed, 2023)
    • 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, ...
    • A Contextually Supported Abnormality Detector for Maritime Trajectories 

      Olesen, Kristoffer Vinther; Boubekki, Ahcene; Kampffmeyer, Michael Christian; Jenssen, Robert; Christensen, Anders Nymark; Hørlück, Sune; Clemmensen, Line H. (Journal article; Peer reviewed, 2023)
      The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal ...
    • Discriminative multimodal learning via conditional priors in generative models 

      Andrade Mancisidor, Rogelio; Kampffmeyer, Michael Christian; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2023)
      Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data, which depict an object from different viewpoints. These two learning mechanisms ...