Zobrazeno 1 - 10
of 3 729
pro vyhledávání: '"WICKSTROM A"'
Autor:
Brüsch, Thea, Wickstrøm, Kristoffer K., Schmidt, Mikkel N., Jenssen, Robert, Alstrøm, Tommy S.
State-of-the-art methods for explaining predictions based on time series are built on learning an instance-wise saliency mask for each time step. However, for many types of time series, the salient information is found in the frequency domain. Adopti
Externí odkaz:
http://arxiv.org/abs/2411.05841
Autor:
Brüsch, Thea, Wickstrøm, Kristoffer K., Schmidt, Mikkel N., Alstrøm, Tommy S., Jenssen, Robert
Time series data is fundamentally important for describing many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop eXplainable AI (XAI) in these domains the
Externí odkaz:
http://arxiv.org/abs/2406.13584
Autor:
Trosten, Daniel J., Chakraborty, Rwiddhi, Løkse, Sigurd, Wickstrøm, Kristoffer Knutsen, Jenssen, Robert, Kampffmeyer, Michael C.
Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where a few points (hubs) occur
Externí odkaz:
http://arxiv.org/abs/2303.09352
Autor:
Hedström, Anna, Bommer, Philine, Wickstrøm, Kristoffer K., Samek, Wojciech, Lapuschkin, Sebastian, Höhne, Marina M. -C.
Publikováno v:
Transactions on Machine Learning Research, Volume 2023, (2023), ISSN: 2835-8856
One of the unsolved challenges in the field of Explainable AI (XAI) is determining how to most reliably estimate the quality of an explanation method in the absence of ground truth explanation labels. Resolving this issue is of utmost importance as t
Externí odkaz:
http://arxiv.org/abs/2302.07265
Publikováno v:
Journal of Statistics and Data Science Education, Vol 32, Iss 2, Pp 161-173 (2024)
AbstractDespite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have inv
Externí odkaz:
https://doaj.org/article/6daecfa084b94da2803029fc52e7e4c8
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
Autor:
Wickstrøm, Kristoffer Knutsen, Østmo, Eirik Agnalt, Radiya, Keyur, Mikalsen, Karl Øyvind, Kampffmeyer, Michael Christian, Jenssen, Robert
Deep learning-based approaches for content-based image retrieval (CBIR) of CT liver images is an active field of research, but suffers from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costl
Externí odkaz:
http://arxiv.org/abs/2207.04812
Autor:
Wickstrøm, Kristoffer, Johnson, J. Emmanuel, Løkse, Sigurd, Camps-Valls, Gustau, Mikalsen, Karl Øyvind, Kampffmeyer, Michael, Jenssen, Robert
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is used to visualize similarities between po
Externí odkaz:
http://arxiv.org/abs/2205.08864
The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing high quality representations could be of great value. It is key to en
Externí odkaz:
http://arxiv.org/abs/2203.09270
Autor:
Wickstrøm, Kristoffer K., Trosten, Daniel J., Løkse, Sigurd, Boubekki, Ahcène, Mikalsen, Karl Øyvind, Kampffmeyer, Michael C., Jenssen, Robert
Despite the significant improvements that representation learning via self-supervision has led to when learning from unlabeled data, no methods exist that explain what influences the learned representation. We address this need through our proposed a
Externí odkaz:
http://arxiv.org/abs/2112.10161
Autor:
Wickstrøm, Kristoffer, Mikalsen, Karl Øyvind, Kampffmeyer, Michael, Revhaug, Arthur, Jenssen, Robert
Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often are very accurate, they have no inherent mechanism for explaining what
Externí odkaz:
http://arxiv.org/abs/2010.11310