82 citations · 112 across the 6 of their papers we have counts for
6 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…
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…
Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
Sigurd Løkse, Filippo Maria Bianchi, Arnt-Børre Salberg +1
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data. By combining posterior distributions of…
Deep Kernelized Autoencoders
Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi +2
In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, use…