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ORCIDKristoffer Wickstrøm
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2020 – today
- 2024
[c6]Bjørn Leth 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. ICML 2024- [i11]Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Tommy S. Alstrøm, Robert Jenssen:
Explaining time series models using frequency masking. CoRR abs/2406.13584 (2024) - 2023
- [j9]Kristoffer Knutsen Wickstrøm
, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Christian Kampffmeyer, Robert Jenssen:
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. Comput. Medical Imaging Graph. 107: 102239 (2023) - [j8]Kristoffer K. Wickstrøm, Sigurd Løkse, Michael C. Kampffmeyer, Shujian Yu, José C. Príncipe, Robert Jenssen:
Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy. Entropy 25(6): 899 (2023) - [j7]Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse, Ahcène Boubekki, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Robert Jenssen:
RELAX: Representation Learning Explainability. Int. J. Comput. Vis. 131(6): 1584-1610 (2023) - [j6]Ane Blázquez-García, Kristoffer Wickstrøm, Shujian Yu, Karl Øyvind Mikalsen, Ahcène Boubekki, Angel Conde, Usue Mori, Robert Jenssen, José Antonio Lozano:
Selective Imputation for Multivariate Time Series Datasets With Missing Values. IEEE Trans. Knowl. Data Eng. 35(9): 9490-9501 (2023) - [j5]Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. Trans. Mach. Learn. Res. 2023 (2023) - [c5]Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse, Kristoffer Knutsen Wickstrøm, Robert Jenssen, Michael C. Kampffmeyer:
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings. CVPR 2023: 7527-7536 - [c4]Eirik Agnalt Østmo, Kristoffer K. Wickstrøm, Keyur Radiya, Michael C. Kampffmeyer, Robert Jenssen:
View it Like a Radiologist: Shifted Windows for Deep Learning Augmentation Of CT Images. MLSP 2023: 1-6 - [i10]Anna Hedström, Philine Lou Bommer, Kristoffer K. Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. CoRR abs/2302.07265 (2023) - [i9]Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse, Kristoffer Knutsen Wickstrøm, Robert Jenssen, Michael C. Kampffmeyer:
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings. CoRR abs/2303.09352 (2023) - [i8]Eirik Agnalt Østmo, Kristoffer K. Wickstrøm, Keyur Radiya, Michael C. Kampffmeyer, Robert Jenssen:
View it like a radiologist: Shifted windows for deep learning augmentation of CT images. CoRR abs/2311.14990 (2023) - 2022
- [j4]Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen:
Mixing up contrastive learning: Self-supervised representation learning for time series. Pattern Recognit. Lett. 155: 54-61 (2022) - [c3]Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen:
The Kernelized Taylor Diagram. NAIS 2022: 125-131 - [i7]Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen:
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series. CoRR abs/2203.09270 (2022) - [i6]Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen:
The Kernelized Taylor Diagram. CoRR abs/2205.08864 (2022) - [i5]Kristoffer Knutsen Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Christian Kampffmeyer, Robert Jenssen:
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. CoRR abs/2207.04812 (2022) - 2021
- [j3]Kristoffer Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen:
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE J. Biomed. Health Informatics 25(7): 2435-2444 (2021) - [j2]Shujian Yu, Kristoffer Wickstrøm, Robert Jenssen, José C. Príncipe:
Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration. IEEE Trans. Neural Networks Learn. Syst. 32(1): 435-442 (2021) - [i4]Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Robert Jenssen:
RELAX: Representation Learning Explainability. CoRR abs/2112.10161 (2021) - 2020
- [j1]Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen:
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps. Medical Image Anal. 60 (2020) - [c2]Van Nhan Nguyen, Sigurd Løkse, Kristoffer Wickstrøm, Michael Kampffmeyer, Davide Roverso, Robert Jenssen:
SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks. ECCV (23) 2020: 118-134 - [i3]Kristoffer Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen:
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. CoRR abs/2010.11310 (2020)
2010 – 2019
- 2019
- [i2]Kristoffer Wickstrøm, Sigurd Løkse, Michael Kampffmeyer, Shujian Yu, José C. Príncipe, Robert Jenssen:
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels. CoRR abs/1909.11396 (2019) - 2018
- [c1]Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen:
Uncertainty Modeling and interpretability in Convolutional Neural Networks for Polyp Segmentation. MLSP 2018: 1-6 - [i1]Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen:
Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps. CoRR abs/1807.10584 (2018)
Coauthor Index
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