108 citations · 220 across the 18 of their papers we have counts for
7 papers · 1 filter
The Kernelized Taylor Diagram
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse +4
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely u…
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen +1
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…
RELAX: Representation Learning Explainability
Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse +4
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 t…
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs
Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Robert Jenssen
A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important informati…
Noisy multi-label semi-supervised dimensionality reduction
Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi +1
Noisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not ac…
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples
Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi +2
A large fraction of the electronic health records consists of clinical measurements collected over time, such as blood tests, which provide important information about the health s…
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruiz +1
Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time se…