Sigurd Løkse | DeepAI

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Gustau Camps-Valls
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Simone Scardapane
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Michael Kampffmeyer
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Filippo Maria Bianchi
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Shujian Yu
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Jose Principe
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∙ 03/17/2023

On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering

Self-supervised learning is a central component in recent approaches to ...

0 Daniel J. Trosten, et al. ∙

∙ 03/16/2023

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings

Distance-based classification is frequently used in transductive few-sho...

0 Daniel J. Trosten, et al. ∙

∙ 01/21/2023

The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2...

0 Shujian Yu, et al. ∙

∙ 05/18/2022

The Kernelized Taylor Diagram

This paper presents the kernelized Taylor diagram, a graphical framework...

4 Kristoffer Wickstrøm, et al. ∙

∙ 12/19/2021

RELAX: Representation Learning Explainability

Despite the significant improvements that representation learning via se...

0 Kristoffer K. Wickstrøm, et al. ∙

∙ 03/13/2021

Reconsidering Representation Alignment for Multi-view Clustering

Aligning distributions of view representations is a core component of to...

0 Daniel J. Trosten, et al. ∙

∙ 09/25/2019

Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels

Analyzing deep neural networks (DNNs) via information plane (IP) theory ...

33 Kristoffer Wickstrøm, et al. ∙

∙ 02/13/2019

Deep Divergence-Based Approach to Clustering

A promising direction in deep learning research consists in learning rep...

36 Michael Kampffmeyer, et al. ∙

∙ 07/19/2018

The Deep Kernelized Autoencoder

Autoencoders learn data representations (codes) in such a way that the i...

0 Michael Kampffmeyer, et al. ∙

∙ 03/21/2018

Reservoir computing approaches for representation and classification of multivariate time series

Classification of multivariate time series (MTS) has been tackled with a...

0 Filippo Maria Bianchi, et al. ∙

∙ 11/17/2017

Bidirectional deep-readout echo state networks

We propose a deep architecture for the classification of multivariate ti...

0 Filippo Maria Bianchi, et al. ∙

∙ 11/17/2017

Bidirectional deep-reservoir echo state networks

We propose a deep architecture for the classification of multivariate ti...

0 Filippo Maria Bianchi, et al. ∙

∙ 11/17/2017

Bidirectional deep echo state networks

In this work we propose a deep architecture for the classification of mu...

0 Filippo Maria Bianchi, et al. ∙

∙ 02/23/2017

Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data

In this paper, we propose PCKID, a novel, robust, kernel function for sp...

0 Sigurd Løkse, et al. ∙

∙ 02/08/2017

Deep Kernelized Autoencoders

In this paper we introduce the deep kernelized autoencoder, a neural net...

0 Michael Kampffmeyer, et al. ∙

∙ 08/16/2016

Training Echo State Networks with Regularization through Dimensionality Reduction

In this paper we introduce a new framework to train an Echo State Networ...

0 Sigurd Løkse, et al. ∙

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