Robert Jenssen

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 10/15/2022

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

The need for interpretable models has fostered the development of self-e...

0 Srishti Gautam, et al. ∙

research

∙ 07/11/2022

A clinically motivated self-supervised approach for content-based image retrieval of CT liver images

Deep learning-based approaches for content-based image retrieval (CBIR) ...

21 Kristoffer Knutsen Wickstrøm, et al. ∙

research

∙ 05/31/2022

Principle of Relevant Information for Graph Sparsification

Graph sparsification aims to reduce the number of edges of a graph while...

0 Shujian Yu, et al. ∙

research

∙ 05/18/2022

The Kernelized Taylor Diagram

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

4 Kristoffer Wickstrøm, et al. ∙

research

∙ 05/07/2022

BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck

Developing a new diagnostic models based on the underlying biological me...

0 Kaizhong Zheng, et al. ∙

research

∙ 03/17/2022

Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series

The lack of labeled data is a key challenge for learning useful represen...

0 Kristoffer Wickstrøm, et al. ∙

research

∙ 03/03/2022

Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels

Recent work has shown that label-efficient few-shot learning through sel...

0 Stine Hansen, et al. ∙

research

∙ 01/10/2022

Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation

The recent trend of integrating multi-source Chest X-Ray datasets to imp...

0 Srishti Gautam, et al. ∙

research

∙ 12/19/2021

RELAX: Representation Learning Explainability

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

0 Kristoffer K. Wickstrøm, et al. ∙

research

∙ 11/06/2021

Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks

Multi-modality data is becoming readily available in remote sensing (RS)...

10 Qinghui Liu, et al. ∙

research

∙ 10/09/2021

Discriminative Multimodal Learning via Conditional Priors in Generative Models

Deep generative models with latent variables have been used lately to le...

0 Rogelio A Mancisidor, et al. ∙

research

∙ 08/27/2021

This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation

Current machine learning models have shown high efficiency in solving a ...

0 Srishti Gautam, et al. ∙

research

∙ 07/07/2021

On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit

The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients ...

0 Óscar Escudero-Arnanz, et al. ∙

research

∙ 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. ∙

research

∙ 01/25/2021

Measuring Dependence with Matrix-based Entropy Functional

Measuring the dependence of data plays a central role in statistics and ...

0 Shujian Yu, et al. ∙

research

∙ 12/07/2020

Joint Optimization of an Autoencoder for Clustering and Embedding

Incorporating k-means-like clustering techniques into (deep) autoencoder...

0 Ahcène Boubekki, et al. ∙

research

∙ 09/03/2020

SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation

Capturing global contextual representations by exploiting long-range pix...

11 Qinghui Liu, et al. ∙

research

∙ 04/21/2020

Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation

We propose a novel architecture called the Multi-view Self-Constructing ...

0 Qinghui Liu, et al. ∙

research

∙ 04/21/2020

The 1st Agriculture-Vision Challenge: Methods and Results

The first Agriculture-Vision Challenge aims to encourage research in dev...

18 Mang Tik Chiu, et al. ∙

research

∙ 04/15/2020

Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images

Image translation with convolutional autoencoders has recently been used...

8 Luigi T. Luppino, et al. ∙

research

∙ 03/15/2020

Self-Constructing Graph Convolutional Networks for Semantic Labeling

Graph Neural Networks (GNNs) have received increasing attention in many ...

0 Qinghui Liu, et al. ∙

research

∙ 02/27/2020

A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

A large fraction of the electronic health records (EHRs) consists of cli...

0 Karl Øyvind Mikalsen, et al. ∙

research

∙ 01/20/2020

Deep Image Clustering with Tensor Kernels and Unsupervised Companion Objectives

In this paper we develop a new model for deep image clustering, using co...

11 Daniel J. Trosten, et al. ∙

research

∙ 01/13/2020

Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

Image translation with convolutional neural networks has recently been u...

25 Luigi Tommaso Luppino, et al. ∙

research

∙ 12/19/2019

LS-Net: Fast Single-Shot Line-Segment Detector

In low-altitude Unmanned Aerial Vehicle (UAV) flights, power lines are c...

13 Van Nhan Nguyen, et al. ∙

research

∙ 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. ∙

research

∙ 09/07/2019

Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network

It is important, but challenging, for the forest industry to accurately ...

0 Qinghui Liu, et al. ∙

research

∙ 08/30/2019

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images

We propose a network for semantic mapping called the Dense Dilated Convo...

0 Qinghui Liu, et al. ∙

research

∙ 07/10/2019

Time series cluster kernels to exploit informative missingness and incomplete label information

The time series cluster kernel (TCK) provides a powerful tool for analys...

0 Karl Øyvind Mikalsen, et al. ∙

research

∙ 03/14/2019

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Learning data representations that reflect the customers' creditworthine...

0 Rogelio A Mancisidor, et al. ∙

research

∙ 02/20/2019

Noisy multi-label semi-supervised dimensionality reduction

Noisy labeled data represent a rich source of information that often are...

0 Karl Øyvind Mikalsen, et al. ∙

research

∙ 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. ∙

research

∙ 11/29/2018

Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series

The task of clustering unlabeled time series and sequences entails a par...

0 Daniel J. Trosten, et al. ∙

research

∙ 08/23/2018

Multivariate Extension of Matrix-based Renyi's α-order Entropy Functional

The matrix-based Renyi's α-order entropy functional was recently introdu...

0 Shujian Yu, et al. ∙

research

∙ 07/19/2018

The Deep Kernelized Autoencoder

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

0 Michael Kampffmeyer, et al. ∙

research

∙ 07/16/2018

Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps

Convolutional Neural Networks (CNNs) are propelling advances in a range ...

0 Kristoffer Wickstrøm, et al. ∙

research

∙ 06/07/2018

Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder

Identifying customer segments in retail banking portfolios with differen...

0 Rogelio Andrade Mancisidor, et al. ∙

research

∙ 05/09/2018

Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders

Learning compressed representations of multivariate time series (MTS) fa...

0 Filippo Maria Bianchi, et al. ∙

research

∙ 04/18/2018

Understanding Convolutional Neural Network Training with Information Theory

Using information theoretic concepts to understand and explore the inner...

0 Shujian Yu, et al. ∙

research

∙ 03/21/2018

An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples

A large fraction of the electronic health records consists of clinical m...

0 Karl Øyvind Mikalsen, et al. ∙

research

∙ 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. ∙

research

∙ 11/17/2017

Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks

Clinical measurements that can be represented as time series constitute ...

0 Andreas Storvik Strauman, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 10/20/2017

Learning compressed representations of blood samples time series with missing data

Clinical measurements collected over time are naturally represented as m...

0 Filippo Maria Bianchi, et al. ∙

research

∙ 09/21/2017

Urban Land Cover Classification with Missing Data Using Deep Convolutional Neural Networks

Automatic urban land cover classification is a classical problem in remo...

0 Michael Kampffmeyer, et al. ∙

Robert Jenssen

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