82 citations · 110 across the 5 of their papers we have counts for
8 papers
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…
Reconsidering Representation Alignment for Multi-view Clustering
Daniel J. Trosten, Sigurd Løkse, Robert Jenssen +1
Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering. However, we identify several drawbacks with na…
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels
Kristoffer Wickstrøm, Sigurd Løkse, Michael Kampffmeyer +3
Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently as a tool to gain insight into, among others, their generalization…
Deep Divergence-Based Approach to Clustering
Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi +3
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminati…
The Deep Kernelized Autoencoder
Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi +2
Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of…
Reservoir computing approaches for representation and classification of multivariate time series
Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse +1
Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios. Reservoir Computing (RC) provides…