
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 ...
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Reconsidering Representation Alignment for Multiview Clustering
Aligning distributions of view representations is a core component of to...
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Measuring Dependence with Matrixbased Entropy Functional
Measuring the dependence of data plays a central role in statistics and ...
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Joint Optimization of an Autoencoder for Clustering and Embedding
Incorporating kmeanslike clustering techniques into (deep) autoencoder...
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SCGNet: SelfConstructing Graph Neural Networks for Semantic Segmentation
Capturing global contextual representations by exploiting longrange pix...
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Multiview SelfConstructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation
We propose a novel architecture called the Multiview SelfConstructing ...
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The 1st AgricultureVision Challenge: Methods and Results
The first AgricultureVision Challenge aims to encourage research in dev...
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CodeAligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
Image translation with convolutional autoencoders has recently been used...
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SelfConstructing Graph Convolutional Networks for Semantic Labeling
Graph Neural Networks (GNNs) have received increasing attention in many ...
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A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs
A large fraction of the electronic health records (EHRs) consists of cli...
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Deep Image Clustering with Tensor Kernels and Unsupervised Companion Objectives
In this paper we develop a new model for deep image clustering, using co...
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Deep Image Translation with an AffinityBased Change Prior for Unsupervised Multimodal Change Detection
Image translation with convolutional neural networks has recently been u...
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LSNet: Fast SingleShot LineSegment Detector
In lowaltitude Unmanned Aerial Vehicle (UAV) flights, power lines are c...
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Information Plane Analysis of Deep Neural Networks via MatrixBased Renyi's Entropy and Tensor Kernels
Analyzing deep neural networks (DNNs) via information plane (IP) theory ...
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Road Mapping In LiDAR Images Using A JointTask Dense Dilated Convolutions Merging Network
It is important, but challenging, for the forest industry to accurately ...
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Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images
We propose a network for semantic mapping called the Dense Dilated Convo...
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Time series cluster kernels to exploit informative missingness and incomplete label information
The time series cluster kernel (TCK) provides a powerful tool for analys...
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Learning Latent Representations of Bank Customers With The Variational Autoencoder
Learning data representations that reflect the customers' creditworthine...
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Noisy multilabel semisupervised dimensionality reduction
Noisy labeled data represent a rich source of information that often are...
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Deep DivergenceBased Approach to Clustering
A promising direction in deep learning research consists in learning rep...
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Recurrent Deep Divergencebased Clustering for simultaneous feature learning and clustering of variable length time series
The task of clustering unlabeled time series and sequences entails a par...
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Multivariate Extension of Matrixbased Renyi's αorder Entropy Functional
The matrixbased Renyi's αorder entropy functional was recently introdu...
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The Deep Kernelized Autoencoder
Autoencoders learn data representations (codes) in such a way that the i...
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Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps
Convolutional Neural Networks (CNNs) are propelling advances in a range ...
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SegmentBased Credit Scoring Using Latent Clusters in the Variational Autoencoder
Identifying customer segments in retail banking portfolios with differen...
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Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
Learning compressed representations of multivariate time series (MTS) fa...
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Understanding Convolutional Neural Network Training with Information Theory
Using information theoretic concepts to understand and explore the inner...
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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...
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Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a...
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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 ...
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Bidirectional deepreadout echo state networks
We propose a deep architecture for the classification of multivariate ti...
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Bidirectional deepreservoir echo state networks
We propose a deep architecture for the classification of multivariate ti...
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Bidirectional deep echo state networks
In this work we propose a deep architecture for the classification of mu...
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Learning compressed representations of blood samples time series with missing data
Clinical measurements collected over time are naturally represented as m...
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Urban Land Cover Classification with Missing Data Using Deep Convolutional Neural Networks
Automatic urban land cover classification is a classical problem in remo...
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An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
The key component in forecasting demand and consumption of resources in ...
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Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Similaritybased approaches represent a promising direction for time ser...
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Spectral Clustering using PCKID  A Probabilistic Cluster Kernel for Incomplete Data
In this paper, we propose PCKID, a novel, robust, kernel function for sp...
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Deep Kernelized Autoencoders
In this paper we introduce the deep kernelized autoencoder, a neural net...
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Temporal Overdrive Recurrent Neural Network
In this work we present a novel recurrent neural network architecture de...
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Multiplex visibility graphs to investigate recurrent neural networks dynamics
A recurrent neural network (RNN) is a universal approximator of dynamica...
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Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Networ...
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Manifold unwrapping using density ridges
Research on manifold learning within a density ridge estimation framewor...
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Optimized Kernel Entropy Components
This work addresses two main issues of the standard Kernel Entropy Compo...
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Robert Jenssen
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Director/Head of the UiT Machine Learning Group, Associate Professor at the University of Tromsø  The Arctic University of Norway, General chair of SCIA 2017, IEEE MLSP Technical Committee, and on the IAPR Governing Board.