Uncertainty Modeling and interpretability in Convolutional Neural Networks for Polyp Segmentation. - Kristoffer Wickstrøm; Michael Kampffmeyer; Robert Jenssen (MLSP, 2018)

Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels. - Kristoffer Wickstrøm; Sigurd Løkse; Michael Kampffmeyer; Shujian Yu; José C. Príncipe; Robert Jenssen (CoRR, 2019)

SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks. - Van Nhan Nguyen; Sigurd Løkse; Kristoffer Wickstrøm; Michael Kampffmeyer; Davide Roverso; Robert Jenssen (ECCV (23), 2020)

Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps. - Kristoffer Wickstrøm; Michael Kampffmeyer; Robert Jenssen (Medical Image Anal., 2020)

RELAX: Representation Learning Explainability. - Kristoffer K. Wickstrøm; Daniel J. Trosten; Sigurd Løkse; Karl Øyvind Mikalsen; Michael C. Kampffmeyer; Robert Jenssen (CoRR, 2021)

Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. - Kristoffer Wickstrøm; Karl Øyvind Mikalsen; Michael Kampffmeyer; Arthur Revhaug; Robert Jenssen (IEEE J. Biomed. Health Informatics, 2021)

Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration. - Shujian Yu; Kristoffer Wickstrøm; Robert Jenssen; José C. Príncipe (IEEE Trans. Neural Networks Learn. Syst., 2021)

The Kernelized Taylor Diagram. - Kristoffer Wickstrøm; Juan Emmanuel Johnson; Sigurd Løkse; Gustau Camps-Valls; Karl Øyvind Mikalsen; Michael Kampffmeyer; Robert Jenssen (NAIS, 2022)

Mixing up contrastive learning: Self-supervised representation learning for time series. - Kristoffer Wickstrøm; Michael Kampffmeyer; Karl Øyvind Mikalsen; Robert Jenssen (Pattern Recognit. Lett., 2022)

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings. - Daniel J. Trosten; Rwiddhi Chakraborty; Sigurd Løkse; Kristoffer Knutsen Wickstrøm; Robert Jenssen; Michael C. Kampffmeyer (CVPR, 2023)

View it Like a Radiologist: Shifted Windows for Deep Learning Augmentation Of CT Images. - Eirik Agnalt Østmo; Kristoffer K. Wickstrøm; Keyur Radiya; Michael C. Kampffmeyer; Robert Jenssen (MLSP, 2023)

A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. - Kristoffer Knutsen Wickstrøm; Eirik Agnalt Østmo; Keyur Radiya; Karl Øyvind Mikalsen; Michael Christian Kampffmeyer; Robert Jenssen (Comput. Medical Imaging Graph., 2023)

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. - Anna Hedström; Philine Lou Bommer; Kristoffer Knutsen Wickstrøm; Wojciech Samek; Sebastian Lapuschkin; Marina M.-C. Höhne (Trans. Mach. Learn. Res., 2023)

Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy. - Kristoffer K. Wickstrøm; Sigurd Løkse; Michael C. Kampffmeyer; Shujian Yu; José C. Príncipe; Robert Jenssen (Entropy, 2023)

RELAX: Representation Learning Explainability. - Kristoffer K. Wickstrøm; Daniel J. Trosten; Sigurd Løkse; Ahcène Boubekki; Karl Øyvind Mikalsen; Michael C. Kampffmeyer; Robert Jenssen (Int. J. Comput. Vis., 2023)

Selective Imputation for Multivariate Time Series Datasets With Missing Values. - Ane Blázquez-García; Kristoffer Wickstrøm; Shujian Yu; Karl Øyvind Mikalsen; Ahcène Boubekki; Angel Conde; Usue Mori; Robert Jenssen; José Antonio Lozano 001 (IEEE Trans. Knowl. Data Eng., 2023)

From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation. - Kristoffer Wickstrøm; Marina M.-C. Höhne; Anna Hedström (ECCV Workshops (21), 2024)

Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks. - Bjørn Leth Møller; Christian Igel; Kristoffer Knutsen Wickstrøm; Jon Sporring; Robert Jenssen; Bulat Ibragimov (ICML, 2024)

Explaining time series models using frequency masking. - Thea Brüsch; Kristoffer K. Wickstrøm; Mikkel N. Schmidt; Tommy S. Alstrøm; Robert Jenssen (CoRR, 2024)

FLEXtime: Filterbank learning for explaining time series. - Thea Brüsch; Kristoffer K. Wickstrøm; Mikkel N. Schmidt; Robert Jenssen; Tommy S. Alstrøm (CoRR, 2024)

REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability. - Kristoffer K. Wickstrøm; Thea Brüsch; Michael C. Kampffmeyer; Robert Jenssen (AAAI, 2025)

AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering. - Jing Wang 116; Songhe Feng; Kristoffer Knutsen Wickstrøm; Michael C. Kampffmeyer (CVPR, 2025)

FreqRISE: Explaining time series using frequency masking. - Thea Brüsch; Kristoffer Knutsen Wickstrøm; Mikkel N. Schmidt; Tommy Sonne Alstrøm; Robert Jenssen (NLDL, 2025)

NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks. - Bjørn Leth Møller; Sepideh Amiri; Christian Igel; Kristoffer Knutsen Wickstrøm; Robert Jenssen; Matthias Keicher; Mohammad Farid Azampour; Nassir Navab; Bulat Ibragimov (NLDL, 2025)

Investigating the Impact of Feature Reduction for Deep Learning-based Seasonal Sea Ice Forecasting. - Lars Uebbing; Harald L. Joakimsen; Luigi Tommaso Luppino; Iver Martinsen; Andrew McDonald 003; Kristoffer Knutsen Wickstrøm; Sébastien Lefèvre; Arnt-Børre Salberg; Scott Hosking; Robert Jenssen (NLDL, 2025)

Assessing the Efficacy of Multi-task Learning in Mammographic Density Classification: A Study on Class Imbalance and Model Performance. - Suaiba Amina Salahuddin; Elisabeth Wetzer; Kristoffer Wickstrøm; Solveig Thrun; Michael Kampffmeyer; Robert Jenssen (SCIA (2), 2025)

From Colors to Classes: Emergence of Concepts in Vision Transformers. - Teresa Dorszewski; Lenka Tetková; Robert Jenssen; Lars Kai Hansen; Kristoffer Knutsen Wickstrøm (CoRR, 2025)

Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction. - Solveig Thrun; Stine Hansen; Zijun Sun; Nele Blum; Suaiba Amina Salahuddin; Kristoffer Wickstrøm; Elisabeth Wetzer; Robert Jenssen; Maik Stille; Michael Kampffmeyer (CoRR, 2025)

A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal [18F]FDG PET imaging. - Christian Salomonsen; Luigi Tommaso Luppino; Fredrik Aspheim; Kristoffer Wickstrøm; Elisabeth Wetzer; Michael Kampffmeyer; Rodrigo Berzaghi; Rune Sundset; Robert Jenssen; Samuel Kuttner (CoRR, 2025)