Ane Blazquez-Garcia,
Kristoffer Knutsen Wickstrøm,
Shujian Yu,
Karl Øyvind Mikalsen,
Ahcene Boubekki,
Angel Conde
et al.:
Selective Imputation for Multivariate Time Series Datasets with Missing Values
IEEE Transactions on Knowledge and Data Engineering 2023
ARKIV /
DOI
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
ARKIV /
DOI
Kristoffer Knutsen Wickstrøm,
Marina Marie-Claire Höhne
:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
Transactions on Machine Learning Research (TMLR) 2023
ARKIV
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
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
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
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
ARKIV /
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 (CCIS) 2022
ARKIV /
DOI
Andreas Kvammen,
Kristoffer Wickstrøm,
Samuel Kociscak,
Jakub Vaverka,
Libor Nouzak,
Arnaud Zaslavsky
et al.:
Machine learning detection of dust impact signals observed by the Solar Orbiter
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
Samuel Kuttner,
Kristoffer Knutsen Wickstrøm,
Mark Lubberink,
Andreas Tolf,
Joachim Burman,
Rune Sundset
et al.:
Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function
Journal of Cerebral Blood Flow and Metabolism 08. February 2021
ARKIV /
DOI
Kristoffer Knutsen Wickstrøm,
Karl Oyvind Mikalsen,
Michael Kampffmeyer,
Arthur Revhaug,
Robert Jenssen
:
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
IEEE journal of biomedical and health informatics 2021
ARKIV /
DOI
Shujian Yu,
Kristoffer Knutsen Wickstrøm,
Robert Jenssen,
Jose Principe
:
Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration
IEEE Transactions on Neural Networks and Learning Systems 2020
DOI
Samuel Kuttner,
Kristoffer Knutsen Wickstrøm,
Gustav Kalda,
Seyed Esmaeil Dorraji,
Montserrat Martin-Armas,
Ana Oteiza
et al.:
Machine learning derived input-function in a dynamic 18F-FDG PET study of mice
Biomedical Engineering & Physics Express 2020
ARKIV /
DOI
Andreas Kvammen,
Kristoffer Knutsen Wickstrøm,
Derek McKay,
Noora Partamies
:
Auroral Image Classification With Deep Neural Networks
Journal of Geophysical Research (JGR): Space Physics 05. October 2020
ARKIV /
DOI
Kristoffer Knutsen Wickstrøm,
Michael C. Kampffmeyer,
Robert Jenssen
:
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps
Kristoffer Knutsen Wickstrøm,
Michael C. Kampffmeyer,
Robert Jenssen
:
Uncertainty modeling and interpretability in convolutional neural networks for polyp segmentation
IEEE Signal Processing Society 2018
ARKIV /
DOI
Kristoffer Knutsen Wickstrøm
:
Muligheter og utfordringer for kunstig intelligens i næringslivet
2024
Kristoffer Knutsen Wickstrøm
:
Forskningsfronten på kunstig intelligens
2024
Kristoffer Wickstrøm
:
The aggregation method matters in faithfulness evaluation of XAI
2023
Kristoffer Knutsen Wickstrøm
:
XAI for understanding of SSL representations
2023
Kristoffer Knutsen Wickstrøm
:
XAI for time series analysis
2023
Andreas Kvammen,
Kristoffer Wickstrøm,
Samuel Kociscak,
Jakub Vaverka,
Libor Nouzák,
Arnaud Zaslavsky
et al.:
Machine learning detection of dust impact signals
2023
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
Kristoffer Wickstrøm
:
Technical aspects of translating AI algorithms into real life medical practice, within the design and implementation of Randomized Controlled Trials
2022
Kristoffer Wickstrøm
:
Hva gjør vi når kunstig intelligens gir oss kunnskap vi ikke forstår?
Forskersonen.no 03. January 2022
Kristoffer Wickstrøm
:
Advancing Deep Learning with Emphasis on Data-Driven Healthcare
UiT Norges arktiske universitet 2022
Kristoffer Wickstrøm,
Juan Emmanuel Johnson,
Sigurd Eivindson Løkse,
Gusatu Camps-Valls,
Karl Øyvind Mikalsen,
Michael Kampffmeyer
et al.:
The Kernelized Taylor Diagram
2022
Kristoffer Wickstrøm,
Eirik Agnalt Østmo,
Karl Øyvind Mikalsen,
Michael Kampffmeyer,
Robert Jenssen
:
Explaining representations for medical image retrieval
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
:
The past, present, and future of XAI
2022
Kristoffer Knutsen Wickstrøm,
Sigurd Eivindson Løkse,
Karl Øyvind Mikalsen,
Michael Kampffmeyer,
Robert Jenssen
:
Towards Explainable Representation Learning
2021
Kristoffer Knutsen Wickstrøm,
Michael Kampffmeyer,
Robert Jenssen
:
Advances in explainable DL & how to model uncertainty in explainability
2021
Kristoffer Knutsen Wickstrøm,
Karl Øyvind Mikalsen,
Michael Kampffmeyer,
Arthur Revhaug,
Robert Jenssen
:
Uncertainty-Aware Deep Ensembles for Explainable Time Series Prediction
2021