Kristoffer Wickstrøm

Orcid: 0000-0003-1395-7154

According to our database1, Kristoffer Wickstrøm authored at least 18 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Explaining time series models using frequency masking.
CoRR, 2024

Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Selective Imputation for Multivariate Time Series Datasets With Missing Values.
IEEE Trans. Knowl. Data Eng., September, 2023

RELAX: Representation Learning Explainability.
Int. J. Comput. Vis., June, 2023

Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy.
Entropy, June, 2023

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus.
Trans. Mach. Learn. Res., 2023

A clinically motivated self-supervised approach for content-based image retrieval of CT liver images.
Comput. Medical Imaging Graph., 2023

View it Like a Radiologist: Shifted Windows for Deep Learning Augmentation Of CT Images.
Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Mixing up contrastive learning: Self-supervised representation learning for time series.
Pattern Recognit. Lett., 2022

The Kernelized Taylor Diagram.
Proceedings of the Nordic Artificial Intelligence Research and Development, 2022

2021
Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration.
IEEE Trans. Neural Networks Learn. Syst., 2021

Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series.
IEEE J. Biomed. Health Informatics, 2021

RELAX: Representation Learning Explainability.
CoRR, 2021

2020
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps.
Medical Image Anal., 2020

SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels.
CoRR, 2019

2018
Uncertainty Modeling and interpretability in Convolutional Neural Networks for Polyp Segmentation.
Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018


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