Kristoffer Wickstrøm
Cited by
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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
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
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
The Kernelized Taylor Diagram
K Wickstrøm, JE Johnson, S Løkse, G Camps-Valls, KØ Mikalsen, ...
arXiv preprint arXiv:2205.08864, 2022
RELAX: Representation Learning Explainability
KK Wickstrøm, DJ Trosten, S Løkse, KØ Mikalsen, MC Kampffmeyer, ...
arXiv preprint arXiv:2112.10161, 2021
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