82 citations · 216 across the 14 of their papers we have counts for
15 papers
Cauchy-Schwarz Divergence Information Bottleneck for Regression
Shujian Yu, Xi Yu, Sigurd Løkse +2
The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum suf…
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…
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse +3
Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are pron…
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
Shujian Yu, Hongming Li, Sigurd Løkse +2
The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional di…
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…
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…
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…
Bidirectional deep-readout echo state networks
Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse +1
We propose a deep architecture for the classification of multivariate time series. By means of a recurrent and untrained reservoir we generate a vectorial representation that embed…
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…
Training Echo State Networks with Regularization through Dimensionality Reduction
Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen
In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series. The method consists in projecting the output of the internal layer of…