164 citations · 397 across the 13 of their papers we have counts for
9 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…
Joint Optimization of an Autoencoder for Clustering and Embedding
Ahcène Boubekki, Michael Kampffmeyer, Robert Jenssen +1
Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternat…
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…
Learning Latent Representations of Bank Customers With The Variational Autoencoder
Rogelio A Mancisidor, Michael Kampffmeyer, Kjersti Aas +1
Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the cre…
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…