Kristoffer Wickstrøm
Bjørn Møller,
Christian Igel,
Kristoffer Knutsen Wickstrøm,
Jon Sporring,
Robert Jenssen,
Bulat Ibragimov
:
Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks
International Conference on Learning Representations 2024
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
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
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
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
Kristoffer Wickstrøm,
Daniel Johansen Trosten,
Sigurd Eivindson Løkse,
Ahcene Boubekki,
Karl Øyvind Mikalsen,
Michael Kampffmeyer
et al.:
RELAX: Representation Learning Explainability
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
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,
Michael Kampffmeyer,
Karl Øyvind Mikalsen,
Robert Jenssen
:
Mixing up contrastive learning: Self-supervised representation learning for time series
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,
Juan Emmanuel Johnson,
Sigurd Eivindson Løkse,
Gusatu Camps-Valls,
Karl Øyvind Mikalsen,
Michael Kampffmeyer
et al.:
The Kernelized Taylor Diagram
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
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
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
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
Andreas Kvammen,
Kristoffer Knutsen Wickstrøm,
Derek McKay,
Noora Partamies
:
Auroral Image Classification With Deep Neural Networks
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
Robert Jenssen,
Kristoffer Knutsen Wickstrøm,
Petter Bjørklund
:
Slik kan kunstig intelligens hjelpe legene
Forskning.no 2024
Kristoffer Knutsen Wickstrøm
:
Kunstig intelligens fra beslutningstøtte til beslutningstaker – hvem er ansvarlig når ting går galt?
2024
Christian Salomonsen,
Kristoffer Knutsen Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
2024
Christian Salomonsen,
Kristoffer Knutsen Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
2024
Christian Salomonsen,
Kristoffer Knutsen Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
2024
Petter Bjørklund,
Kristoffer Knutsen Wickstrøm,
Keyur Radiya
:
Finner leverkreft med kunstig intelligens
uit.no 2024
Kristoffer Knutsen Wickstrøm,
Keyur Radiya
:
Finner leverkreft med kunstig intelligens
uit.no 2024
Kristoffer Knutsen Wickstrøm
:
Facebook skal merke KI-bilder: – Stor nyhet
07. Feb 2024
Kristoffer Knutsen Wickstrøm
:
Morrasendinga fra NRK i Troms - Forskere ved UiT Norges arktiske universitet utvikler kunstig intelligens til å finne leverkreft.
10. Jun 2024
Kristoffer Knutsen Wickstrøm
:
Kunstig Intelligens, utfordringer og muligheter for næringslivet
2024
Lars Uebbing,
Harald Lykke Joakimsen,
Luigi Tommaso Luppino,
Iver Martinsen,
Andrew McDonald,
Kristoffer Knutsen Wickstrøm
et al.:
Investigating the Impact of Feature Reduction for Deep Learning-based Seasonal Sea Ice Forecasting
2024
Kristoffer Knutsen Wickstrøm
:
Muligheter og utfordringer for kunstig intelligens i næringslivet
2024
Kristoffer Knutsen Wickstrøm
:
Forskningsfronten på kunstig intelligens
2024
Petter Bjørklund,
Robert Jenssen,
Kristoffer Knutsen Wickstrøm
:
KI har superkrefter som kan hjelpe legene våre
uit.no 2024
Andreas Kvammen,
Kristoffer Wickstrøm,
Samuel Kociscak,
Jakub Vaverka,
Libor Nouzák,
Arnaud Zaslavsky
et al.:
Machine learning detection of dust impact signals
2023
Kristoffer Wickstrøm
:
The aggregation method matters in faithfulness evaluation of XAI
2023
Robert Jenssen,
Kristoffer Knutsen Wickstrøm
:
Ja takk til «krysskulturelle» prosjekter drevet fram av teknologiutvikling
Khrono.no 2023
Robert Jenssen,
Kristoffer Knutsen Wickstrøm
:
Hvordan bør fotavtrykket av regjeringens satsing på kunstig intelligens se ut i Nord-Norge i 2030?
Nordnorsk Debatt 2023
Kristoffer Knutsen Wickstrøm
:
XAI for time series analysis
2023
Kristoffer Knutsen Wickstrøm
:
XAI for understanding of SSL representations
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
:
Advancing Deep Learning with Emphasis on Data-Driven Healthcare
UiT Norges arktiske universitet 2022
Kristoffer Wickstrøm
:
The past, present, and future of XAI
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,
Juan Emmanuel Johnson,
Sigurd Eivindson Løkse,
Gusatu Camps-Valls,
Karl Øyvind Mikalsen,
Michael Kampffmeyer
et al.:
The Kernelized Taylor Diagram
2022
Kristoffer Wickstrøm
:
Hva gjør vi når kunstig intelligens gir oss kunnskap vi ikke forstår?
Forskersonen.no 03. Jan 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 Knutsen Wickstrøm,
Sigurd Eivindson Løkse,
Karl Øyvind Mikalsen,
Michael Kampffmeyer,
Robert Jenssen
:
Towards Explainable Representation Learning
2021
Kristoffer Knutsen Wickstrøm,
Karl Øyvind Mikalsen,
Michael Kampffmeyer,
Arthur Revhaug,
Robert Jenssen
:
Uncertainty-Aware Deep Ensembles for Explainable Time Series Prediction
2021
Kristoffer Knutsen Wickstrøm,
Michael Kampffmeyer,
Robert Jenssen
:
Advances in explainable DL & how to model uncertainty in explainability
2021
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