Robert Jenssen


Professor / Maskinlæring / Senterleder Visual Intelligence

Stillingsbeskrivelse

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

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

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

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

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 m.fl.:
    RELAX: Representation Learning Explainability
    International Journal of Computer Vision 2023 ARKIV / 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 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, 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 m.fl.:
    Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
    IEEE Transactions on Neural Networks and Learning Systems 12. mai 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 m.fl.:
    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 m.fl.:
    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 m.fl.:
    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 m.fl.:
    The Kernelized Taylor Diagram
    2022
  • Changkyu Choi, Shujian Yu, Michael Kampffmeyer, Arnt-Børre Salberg, Nils Olav Handegard, Suaiba Amina Salahuddin m.fl.:
    Explaining Marine Acoustic Target Classification in Multi-channel Echosounder Data using Self-attention Mask, Information-Bottleneck, and Mask Prior
    2022

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