280 citations · 1.7k across the 60 of their papers we have counts for
18 papers · 1 filter
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
Daniel J. Trosten, Sigurd Løkse, Robert Jenssen +1
Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-b…
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…
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…
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…
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…
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…
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…
Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series
Daniel J. Trosten, Andreas S. Strauman, Michael Kampffmeyer +1
The task of clustering unlabeled time series and sequences entails a particular set of challenges, namely to adequately model temporal relations and variable sequence lengths. If t…
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…
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…
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…
Manifold unwrapping using density ridges
Jonas Nordhaug Myhre, Matineh Shaker, Devrim Kaba +2
Research on manifold learning within a density ridge estimation framework has shown great potential in recent work for both estimation and de-noising of manifolds, building on the…
Optimized Kernel Entropy Components
Emma Izquierdo-Verdiguier, Valero Laparra, Robert Jenssen +2
This work addresses two main issues of the standard Kernel Entropy Component Analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gau…