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Bilde av Jenssen, Robert
Bilde av Jenssen, Robert
Department of Physics and Technology robert.jenssen@uit.no +4777646493 41699612 Here you can find me

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

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:

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

 

  • 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, Robert Jenssen, Nils Olav Handegard, Arnt-Børre Salberg :
    Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data
    IEEE Journal of Oceanic Engineering 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
  • 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 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 2023
  • 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
  • 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
  • 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
  • Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen :
    Mixing up contrastive learning: Self-supervised representation learning for time series
    Pattern Recognition Letters 2022 ARKIV / DOI
  • Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen :
    Generating customer's credit behavior with deep generative models
    Knowledge-Based Systems 2022 ARKIV / DOI
  • Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer :
    Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels
    Medical Image Analysis 2022 ARKIV / DOI
  • Luigi Tommaso Luppino, Mads Adrian Hansen, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Robert Jenssen et al.:
    Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
    IEEE Transactions on Neural Networks and Learning Systems 12. May 2022 DOI
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
    IEEE International Symposium on Biomedical Imaging 2022 DOI
  • Huamin Ren, Xiaomeng Su, Robert Jenssen, Jingyue Li, Stian Normann Anfinsen :
    Attention-guided Temporal Convolutional Network for Non-intrusive Load Monitoring
    IEEE (Institute of Electrical and Electronics Engineers) 2022 ARKIV / DOI
  • Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, Jose C. Principe :
    Principle of Relevant Information for Graph Sparsification
    Proceedings of Machine Learning Research (PMLR) 2022 FULLTEKST / DOI
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    Communications in Computer and Information Science 2022 ARKIV / DOI
  • Suaiba Amina Salahuddin, Stine Hansen, Srishti Gautam, Michael Kampffmeyer, Robert Jenssen :
    A self-guided anomaly detection-inspired few-shot segmentation network
    CEUR Workshop Proceedings 2022 ARKIV
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
    Pattern Recognition 2022 ARKIV / DOI
  • Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina Marie-Claire Hohne et al.:
    ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
    Advances in Neural Information Processing Systems 2022 DOI
  • Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt Børre Salberg :
    Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks
    International Journal of Remote Sensing 2022 DOI
  • Ahcene Boubekki, Jonas Nordhaug Myhre, Luigi Tommaso Luppino, Karl Øyvind Mikalsen, Arthur Revhaug, Robert Jenssen :
    Clinically relevant features for predicting the severity of surgical site infections
    IEEE journal of biomedical and health informatics 2021 ARKIV / FULLTEKST / DOI
  • 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
  • Robert Jenssen :
    Visual Intelligence for medical image analysis
    2022
  • Robert Jenssen :
    Presentation of SFI Visual Intelligence
    2022
  • Robert Jenssen :
    Etikk og AI
    2022
  • Robert Jenssen :
    Centre for Research-based Innovation Visual Intelligence and the General Electric Vingmed Ultrasound Collaboration
    2022
  • Robert Jenssen :
    Computer vision for power line monitoring
    2022
  • Robert Jenssen :
    An introduction to self-supervised learning with examples of use cases in few-shot learning and medical image analysis
    2022
  • Robert Jenssen :
    Innovasjon i Visual Intelligence
    2022
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
    2022
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Artifact Detection with Prototypical Relevance Propagation
    2022
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
    2022
  • Samuel Kuttner, Luigi Tommaso Luppino, Kristoffer Wickstrøm, Nils Thomas Doherty Midtbø, Seyed Esmaeil Dorraji, Ana Oteiza et al.:
    Deep learning derived input function in dynamic 18F-FDG PET imaging of mice
    2022
  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Explaining representations for medical image retrieval
    2022
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    2022
  • Changkyu Choi, Shujian Yu, Michael Kampffmeyer, Arnt-Børre Salberg, Nils Olav Handegard, Suaiba Amina Salahuddin et al.:
    Explaining Marine Acoustic Target Classification in Multi-channel Echosounder Data using Self-attention Mask, Information-Bottleneck, and Mask Prior
    2022

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