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Prodekan-Fred-Godtliebsen
Godtliebsen, Fred

Professor i statistikk. 

 

Professor
Bilde av Jenssen, Robert
Jenssen, Robert

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:

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

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

 

Professor / Machine Learning / Centre Leader Visual Intelligence
Juha Vierinen
Vierinen, Juha

I am an experimentalist that works across a range of themes within space physics. I specialize in development of novel radar and radio remote sensing measurement techniques, and apply them to scientific studies in the field of space plasma physics, atmospheric physics, space debris, and planetary science. Much of this work is conducted with high power large aperture radars, ionospheric heating facilities, ionosonde networks, meteor radar networks, radio telescopes, and global navigation satellite networks.

I am not involved in any formal capacity with EISCAT or EISCAT 3D. My relationship to EISCAT is that of a scientific user or potential future scientific user. Please address any media requests about EISCAT and EISCAT 3D to those that are formally leading the project.

Førsteamanuensis / Romfysikk
Førsteaman.-Elinor-Ytterstad
Ytterstad, Elinor
Førsteamanuensis