Robert Jenssen


Professor / Machine Learning / Centre Leader Visual Intelligence

Job description

Director, Visual Intelligence. Visual Intelligence is a Centre for Research-based Innovation (SFI) funded by the Research Council of Norway and a consortium of private and public partners. We are at the international forefront in deep learning research for complex image analysis. Please see

SFI Visual Intelligence

Twitter: @SFI_VI

Co-Director, Integreat. Integreat is a Centre of Excellence (SFF) funded by the Research Council of Norway and the university partners, the University of Oslo and UiT The Arctic University of Norway. We are at the international forefront in knowledge-based machine learning. Please see

SFF Integreat SFF Integreat

Professor, UiT Machine Learning Group. Please see

UiT Machine Learning Group

Adjunct Professor:

Pioneer Centre for AI, University of Copenhagen

Norwegian Computing Center

Selected recent publications:

Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks. ICML 2024. (coordinates coming)

MAP IT to visualize representations. ICLR 2024. https://openreview.net/pdf?id=OKf6JtXtoy

Cauchy-Schwarz divergence information bottleneck for regression. ICLR, 2024. https://openreview.net/pdf?id=7wY67ZDQTE

ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement. Medical Image Analysis, 2023. https://doi.org/10.1016/j.media.2023.102870

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning With Hyperspherical Embeddings. CVPR, 2023. https://openaccess.thecvf.com/content/CVPR2023/html/Trosten_Hubs_and_Hyperspheres_Reducing_Hubness_and_Improving_Transductive_Few-Shot_Learning_CVPR_2023_paper.html

On the Effects of Self-Supervision and Contrastive Alignment in Deep Multi-View Clustering. CVPR, 2023. https://openaccess.thecvf.com/content/CVPR2023/html/Trosten_On_the_Effects_of_Self-Supervision_and_Contrastive_Alignment_in_Deep_CVPR_2023_paper.html

RELAX: Representation Learning Explainability. International Journal of Computer Vision, 2023. https://doi.org/10.1007/s11263-023-01773-2

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. NeurIPS, 2022. https://openreview.net/forum?id=L8pZq2eRWvX

Principle of Relevant Information for Graph Sparsification. UAI, 2022. https://proceedings.mlr.press/v180/yu22c.html

Anomaly Detection-inspired Few-shot Medical Image Segmentation through Self-supervision with Supervoxels. Medical Image Analysis, 2022. https://doi.org/10.1016/j.media.2022.102385

Clinically Relevant Features for Predicting the Severity of Surgical Site Infections. IEEE Journal of Biomedical and Health Informatics, 2021. https://doi.org/10.1109/JBHI.2021.3121038

Joint Optimization of an Autoencoder for Clustering and Embedding. Machine Learning, 2021. https://doi.org/10.1007/s10994-021-06015-5

Measuring Dependence with Matrix-based Entropy Functional. AAAI, 2021. https://doi.org/10.1609/aaai.v35i12.17288

Reconsidering Representation Alignment for Multi-view Clustering. CVPR, 2021. https://openaccess.thecvf.com/content/CVPR2021/papers/Trosten_Reconsidering_Representation_Alignment_for_Multi-View_Clustering_CVPR_2021_paper.pdf

Uncertainty-aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE Journal of Biomedical and Health Informatics, 2020. https://doi.org/10.1109/JBHI.2020.3042637

SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks. ECCV, 2020. https://link.springer.com/chapter/10.1007/978-3-030-58592-1_8

Google Scholar Profile

 



  • Duy Khoi Tran, van Nhan Nguyen, Davide Roverso, Robert Jenssen, Michael Christian Kampffmeyer :
    LSNetv2: Improving weakly supervised power line detection with bipartite matching
    Expert Systems With Applications 2024 DOI
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Robert Jenssen, Michael Christian Kampffmeyer :
    Leveraging tensor kernels to reduce objective function mismatch in deep clustering
    Pattern Recognition 2024 DOI
  • Harald Lykke Joakimsen, Iver Martinsen, Luigi Tommaso Luppino, Andrew McDonald, Scott Hosking, Robert Jenssen :
    Interrogating Sea Ice Predictability with Gradients
    IEEE Geoscience and Remote Sensing Letters 2024 DOI
  • Samuel Kuttner, Luigi Tommaso Luppino, Laurence Convert, Otman Sarrhini, Roger Lecomte, Michael Christian Kampffmeyer et al.:
    Deep learning derived input function in dynamic [18F]FDG PET imaging of mice
    Frontiers in Nuclear Medicine 2024 DOI
  • Stine Hansen, Srishti Gautam, Suaiba Amina Salahuddin, Michael Christian Kampffmeyer, Robert Jenssen :
    ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement
    Medical Image Analysis 2023 ARKIV / DOI
  • Ane Blazquez-Garcia, Kristoffer Knutsen Wickstrøm, Shujian Yu, Karl Øyvind Mikalsen, Ahcene Boubekki, Angel Conde et al.:
    Selective Imputation for Multivariate Time Series Datasets with Missing Values
    IEEE Transactions on Knowledge and Data Engineering 2023 ARKIV / DOI
  • Eirik Agnalt Østmo, Kristoffer Wickstrøm, Keyur Radiya, Michael Kampffmeyer, Robert Jenssen :
    View it like a radiologist: Shifted windows for deep learning augmentation of CT images
    Machine Learning for Signal Processing 2023 ARKIV / DOI
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Robert Jenssen, Michael Kampffmeyer :
    On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
    Computer Vision and Pattern Recognition 22. August 2023 ARKIV / DATA / DOI
  • Kristoffer Wickstrøm, Sigurd Eivindson Løkse, Michael Kampffmeyer, Shujian Yu, José C. Príncipe, Robert Jenssen :
    Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy
    Entropy 2023 ARKIV / DOI
  • Durgesh Kumar Singh, Ahcene Boubekki, Robert Jenssen, Michael Kampffmeyer :
    Supercm: Revisiting Clustering for Semi-Supervised Learning
    Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2023 ARKIV / DOI
  • Kristoffer Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Ahcene Boubekki, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    RELAX: Representation Learning Explainability
    International Journal of Computer Vision 2023 ARKIV / DOI
  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
    Computerized Medical Imaging and Graphics 2023 ARKIV / DOI
  • Changkyu Choi, Michael Kampffmeyer, Nils Olav Handegard, Arnt-Børre Salberg, Robert Jenssen :
    Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data
    IEEE Journal of Oceanic Engineering 2023 ARKIV / DOI
  • Daniel Johansen Trosten, Rwiddhi Chakraborty, Sigurd Eivindson Løkse, Kristoffer Wickstrøm, Robert Jenssen, Michael Kampffmeyer :
    Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
    Computer Vision and Pattern Recognition 2023 ARKIV / DOI
  • Rogelio Andrade Mancisidor, Michael Christian Kampffmeyer, Kjersti Aas, Robert Jenssen :
    Discriminative multimodal learning via conditional priors in generative models
    Neural Networks 2023 ARKIV / DOI
  • Kristoffer Vinther Olesen, Ahcene Boubekki, Michael Christian Kampffmeyer, Robert Jenssen, Anders Nymark Christensen, Sune Hørlück et al.:
    A Contextually Supported Abnormality Detector for Maritime Trajectories
    Journal of Marine Science and Engineering (JMSE) 2023 ARKIV / DOI
  • Fredrik Emil Aspheim, Samuel Kuttner, Luigi Tommaso Luppino, Rune Sundset, Michael Christian Kampffmeyer, Robert Jenssen :
    Deep learning derived input-function in dynamic PET-imaging
    2023
  • Fredrik Emil Aspheim, Luigi Tommaso Luppino, Michael Christian Kampffmeyer, Robert Jenssen, Rune Sundset, Akos Samuel Kuttner :
    Interpretable deep learning model for input function estimation in small-animal 18F-FDG PET imaging
    2023
  • Robert Jenssen :
    XAI for representation learning
    2023
  • Robert Jenssen :
    On representation learning with information theoretic criteria and a new method for representation learning interpretability
    2023
  • Robert Jenssen :
    Information theoretic approaches: To clustering, graph neural networks and for investigating the dynamics of learning
    2023
  • Robert Jenssen :
    Self-supervised learning with XAI
    2023
  • Robert Jenssen :
    Information Theory Meets Deep Learning
    2023
  • Robert Jenssen :
    Deep learning in image analysis, graphs, and a new measure of statistical dependency between graphs
    2023
  • Robert Jenssen :
    XAI for representation learning
    2023
  • Robert Jenssen :
    Kunstig intelligens i helse
    2023
  • Robert Jenssen :
    Kunstig intelligens – Hva er det? Hvor kan det (mis)brukes
    2023
  • Robert Jenssen :
    NRK-intervju Arendalsuka
    16. August 2023
  • Robert Jenssen :
    Kunstig intelligens i fremtidens helsetjenester
    30. September 2023
  • Robert Jenssen :
    A major Norwegian hub for AI research in health
    2023
  • Robert Jenssen :
    Stortinget: Kunstig intelligens i helse
    2023
  • Robert Jenssen :
    En offensiv offentlig politikk for kunstig intelligens i helsetjenesten
    2023
  • Robert Jenssen :
    Deltaker i podcasten KA i KI
    08. December 2023
  • Harald Lykke Joakimsen, Iver Martinsen, Luigi Tommaso Luppino, Robert Jenssen :
    "Explainable" IceNet with backpropagated gradients
    2023
  • Changkyu Choi, Michael Christian Kampffmeyer, Nils Olav Handegard, Arnt-Børre Salberg, Robert Jenssen :
    Deep Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data
    2023
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    RELAX: Representation Learning Explainability
    2022
  • Kristoffer Knutsen Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    RELAX: Representation Learning Explainability
    2022
  • Daniel Johansen Trosten, Kristoffer Wickstrøm, Shujian Yu, Sigurd Eivindson Løkse, Robert Jenssen, Michael Kampffmeyer :
    Deep Clustering with the Cauchy-Schwarz Divergence
    2022
  • Robert Jenssen :
    Pasientnær kunstig intelligens
    2022
  • Robert Jenssen :
    Visual Intelligence and environmental monitoring
    2022
  • Robert Jenssen :
    Towards XAI and Visual Intelligence for health
    2022
  • Robert Jenssen :
    Visual Intelligence advances deep learning research towards innovations
    2022
  • Robert Jenssen :
    Women in AI panel debate
    2022
  • Robert Jenssen :
    Towards XAI and Visual Intelligence for health
    2022
  • Robert Jenssen :
    Learning from limited data
    2022
  • Robert Jenssen :
    Machine Learning Research, AI Technology and Ethical Considerations
    2022
  • Robert Jenssen :
    Visual Intelligence and graph neural networks
    2022
  • Robert Jenssen :
    Mot framtidas helsevesen med kunstig intelligens
    2022
  • Robert Jenssen :
    Kunstig intelligens-utdanninger for helse ved UiT
    2022
  • Robert Jenssen :
    Paneldebatt framtidas helsevesen med AI
    2022

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