Kristoffer Wickstrøm
Kristoffer Wickstrøm
Postdoctoral research fellow at UiT Machine Learning Group
Verified email at - Homepage
Cited by
Cited by
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps
K Wickstrøm, M Kampffmeyer, R Jenssen
Medical Image Analysis 60, 101619, 2020
Understanding convolutional neural networks with information theory: An initial exploration
S Yu, K Wickstrøm, R Jenssen, JC Principe
IEEE Transactions on Neural Networks and Learning Systems, 2020
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels
K Wickstrøm, S Løkse, M Kampffmeyer, S Yu, J Principe, R Jenssen
arXiv preprint arXiv:1909.11396, 2019
SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks
VN Nguyen, S Løkse, K Wickstrøm, M Kampffmeyer, D Roverso, ...
European Conference on Computer Vision, 2020
Auroral image classification with deep neural networks
A Kvammen, K Wickstrøm, D McKay, N Partamies
Journal of Geophysical Research: Space Physics 125 (10), e2020JA027808, 2020
Uncertainty-aware deep ensembles for reliable and explainable predictions of clinical time series
K Wickstrøm, KØ Mikalsen, M Kampffmeyer, A Revhaug, R Jenssen
IEEE Journal of Biomedical and Health Informatics 25 (7), 2435-2444, 2020
Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function
S Kuttner, KK Wickstrøm, M Lubberink, A Tolf, J Burman, R Sundset, ...
Journal of Cerebral Blood Flow & Metabolism 41 (9), 2229-2241, 2021
Mixing up contrastive learning: Self-supervised representation learning for time series
K Wickstrøm, M Kampffmeyer, KØ Mikalsen, R Jenssen
Pattern Recognition Letters 155, 54-61, 2022
Machine learning derived input-function in a dynamic 18F-FDG PET study of mice
S Kuttner, KK Wickstrøm, G Kalda, SE Dorraji, M Martin-Armas, A Oteiza, ...
Biomedical Physics & Engineering Express 6 (1), 015020, 2020
Machine learning detection of dust impact signals observed by the Solar Orbiter
A Kvammen, K Wickstrøm, S Kociscak, J Vaverka, L Nouzak, A Zaslavsky, ...
Annales Geophysicae 41 (1), 69-86, 2023
Relax: Representation learning explainability
KK Wickstrøm, DJ Trosten, S Løkse, A Boubekki, K Mikalsen, ...
International Journal of Computer Vision, 1-27, 2023
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
KK Wickstrøm, EA Østmo, K Radiya, KØ Mikalsen, MC Kampffmeyer, ...
arXiv preprint arXiv:2207.04812, 2022
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
DJ Trosten, R Chakraborty, S Løkse, KK Wickstrøm, R Jenssen, ...
arXiv preprint arXiv:2303.09352, 2023
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
A Hedström, P Bommer, KK Wickstrøm, W Samek, S Lapuschkin, ...
arXiv preprint arXiv:2302.07265, 2023
The Kernelized Taylor Diagram
K Wickstrøm, JE Johnson, S Løkse, G Camps-Valls, KØ Mikalsen, ...
Nordic Artificial Intelligence Research and Development: 4th Symposium of …, 2023
Selective imputation for multivariate time series datasets with missing values
A Blázquez-García, K Wickstrøm, S Yu, KØ Mikalsen, A Boubekki, ...
IEEE Transactions on Knowledge and Data Engineering, 2023
Advancing Deep Learning with Emphasis on Data-Driven Healthcare
KK Wickstrøm
UiT Norges arktiske universitet, 2022
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