R. Jenssen

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Understanding Convolutional Neural Network Training with Information Theory pdf

Robert Jenssen, José Carlos Príncipe, Shujian Yu, 2018, ArXiv.

Towards a unification of information theoretic learning and kernel methods

Deniz Erdoğmuş, J. Príncipe, R. Jenssen, 2004, Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004..

Clustering using Renyi's entropy

Deniz Erdoğmuş, J. Príncipe, R. Jenssen, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Measuring Dependence with Matrix-based Entropy Functional

Robert Jenssen, Jose C. Principe, Shujian Yu, 2021, AAAI.

Multivariate density estimation with optimal marginal parzen density estimation and gaussianization

Deniz Erdoğmuş, J. Príncipe, R. Jenssen, 2004, Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004..

Spectral feature projections that maximize Shannon mutual information with class labels

Robert Jenssen, Deniz Erdogmus, Umut Ozertem, 2006, Pattern Recognit..

Kernel Entropy Component Analysis for Remote Sensing Image Clustering

G. Camps-Valls, R. Jenssen, L. Gómez-Chova, 2012, IEEE Geoscience and Remote Sensing Letters.

1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE

R. Jenssen, T. Cocks, A. Stewart, 1998 .

An Information Theoretic Perspective to Kernel K-Means

R. Jenssen, T. Eltoft, 2006, 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing.

ICA FILTER BANK FOR SEGMENTATION OF TEXTURED IMAGES

Robert Jenssen, Torbjørn Eltoft, R. Jenssen, 2003 .

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

Robert Jenssen, Filippo Maria Bianchi, Arthur Revhaug, 2019, Pattern Recognit..

Manifold unwrapping using density ridges

Jonas Nordhaug Myhre, Deniz Erdoğmuş, R. Jenssen, 2016, 1604.01602.

Multivariate Extension of Matrix-Based Rényi's

$\alpha$

-Order Entropy Functional

J. Príncipe, Shujian Yu, R. Jenssen, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Principle of Relevant Information for Graph Sparsification

J. Príncipe, Shujian Yu, R. Jenssen, 2022, UAI.

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

Michael C. Kampffmeyer, R. Jenssen, Kristoffer Wickstrøm, 2018, Medical Image Anal..

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

Shujian Yu, R. Jenssen, Badong Chen, 2022, ArXiv.

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

Karl Øyvind Mikalsen, Michael C. Kampffmeyer, R. Jenssen, 2022, Comput. Medical Imaging Graph..

LS-Net: fast single-shot line-segment detector

Van Nhan Nguyen, R. Jenssen, D. Roverso, 2019, Machine Vision and Applications.

Information theoretic clustering: a unifying review of three recent algorithms

J.C. Principe, R. Jenssen, T. Eltoft, 2004, Proceedings of the 6th Nordic Signal Processing Symposium, 2004. NORSIG 2004..

Discriminative Multimodal Learning via Conditional Priors in Generative Models

Michael C. Kampffmeyer, R. Jenssen, K. Aas, 2021, ArXiv.

An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting pdf

Robert Jenssen, Antonello Rizzi, Filippo Maria Bianchi, 2017, ArXiv.

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

Marina M.-C. Höhne, Michael C. Kampffmeyer, R. Jenssen, 2022, NeurIPS.

Principle of Relevant Information for Graph Sparsification (Supp. Material)

J. Príncipe, Shujian Yu, R. Jenssen, 2022 .

Image Analysis

Robert Jenssen, Jon Yngve Hardeberg, Arnt-Borre Salberg, 2009 .

A self-guided anomaly detection-inspired few-shot segmentation network

Michael C. Kampffmeyer, R. Jenssen, Srishti Gautam, 2022, CVCS.

Discriminative Multimodal Learning via Conditional Priors in Generative Models

Michael C. Kampffmeyer, R. Jenssen, K. Aas, 2021, Neural Networks.

Recurrent Neural Networks for Short-Term Load Forecasting

Robert Jenssen, Antonello Rizzi, Filippo Maria Bianchi, 2017, SpringerBriefs in Computer Science.

UNCERTAINTY MODELING AND INTERPRETABILITY IN CONVOLUTIONAL NEURAL NETWORKS FOR POLYP SEGMENTATION

Michael Kampffmeyer, Robert Jenssen, Kristoffer Wickstrøm, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

A Reproducing Kernel Hilbert Space Framework for ITL

Robert Jenssen, Jian-Wu Xu, Il Memming Park, 2010, Information Theoretic Learning.

Information Theoretic Learning and Kernel Methods

Robert Jenssen, R. Jenssen, 2009 .

Gaussianization: An Efficient Multivariate Density Estimation Technique for Statistical Signal Processing

Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, 2006, J. VLSI Signal Process..

Independent component analysis for texture segmentation

Robert Jenssen, Torbjørn Eltoft, R. Jenssen, 2003, Pattern Recognit..

SPARSE CODE SHRINKAGE BASED ON THE NORMAL INVERSE GAUSSIAN DENSITY MODEL

Robert Jenssen, Torbjørn Eltoft, Alfred Hanssen, 2001 .

Clustering with ITL Principles

Robert Jenssen, Sudhir Rao, R. Jenssen, 2010, Information Theoretic Learning.

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

Robert Jenssen, Filippo Maria Bianchi, Arthur Revhaug, 2018, ArXiv.

Noisy multi-label semi-supervised dimensionality reduction

Robert Jenssen, Filippo Maria Bianchi, Cristina Soguero-Ruíz, 2019, Pattern Recognit..

Mean shift spectral clustering

Robert Jenssen, Deniz Erdogmus, Umut Ozertem, 2008, Pattern Recognit..

Information theoretic spectral clustering

J.C. Principe, R. Jenssen, T. Eltoft, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

RELAX: Representation Learning Explainability

Karl Øyvind Mikalsen, Daniel J. Trosten, Michael C. Kampffmeyer, 2021, International Journal of Computer Vision.

Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data

Robert Jenssen, Stein Olav Skrøvseth, Gregor Stiglic, 2019, Comput. Math. Methods Medicine.

Training Echo State Networks with Regularization Through Dimensionality Reduction

Robert Jenssen, Filippo Maria Bianchi, Sigurd Løkse, 2016, Cognitive Computation.

Synthetic Time Series

Robert Jenssen, Antonello Rizzi, Filippo Maria Bianchi, 2017 .

Information Theoretic Clustering

José Carlos Príncipe, Erhan Gokcay, J. Príncipe, 2002, IEEE Trans. Pattern Anal. Mach. Intell..

Experimental Upper Bound for Convolutive Mixtures of Speech by Maximizing SIR

R. Jenssen, K.E. Hild, R. Jenssen, 2006, 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing.

A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines

Robert Jenssen, Klaus-Robert Müller, Alexander Zien, 2012, PloS one.

A new scatter-based multi-class support vector machine

Robert Jenssen, Klaus-Robert Müller, Alexander Zien, 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning

Robert Jenssen, Van Nhan Nguyen, Davide Roverso, 2019, IEEE Power and Energy Technology Systems Journal.

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

Gabriele Moser, Michael Kampffmeyer, Robert Jenssen, 2020, IEEE Transactions on Neural Networks and Learning Systems.

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

Gabriele Moser, Michael Kampffmeyer, Robert Jenssen, 2020, IEEE Transactions on Geoscience and Remote Sensing.

Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods

José Luis Rojo-Álvarez, Robert Jenssen, Stein Olav Skrøvseth, 2016, J. Biomed. Informatics.

Robust clustering using a kNN mode seeking ensemble

Robert Jenssen, Karl Øyvind Mikalsen, Jonas Nordhaug Myhre, 2018, Pattern Recognit..

Using anchors from free text in electronic health records to diagnose postoperative delirium

Robert Jenssen, Stein Olav Skrøvseth, Fred Godtliebsen, 2017, Comput. Methods Programs Biomed..

Joint optimization of an autoencoder for clustering and embedding

Michael C. Kampffmeyer, Ulf Brefeld, R. Jenssen, 2020, Machine Learning.

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

Michael Kampffmeyer, Robert Jenssen, Arnt-Borre Salberg, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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

Michael Kampffmeyer, Robert Jenssen, Srishti Gautam, 2021, Pattern Recognit..

The AIG/HyVista 1999 USA HyMap Group Shoot: overview and analysis examples

A. B. Lefkoff, F.A. Kruse, J.W. Boardman, 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation

Robert Jenssen, Gustau Camps-Valls, Katalin Blix, 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

The 1st Agriculture-Vision Challenge: Methods and Results

Yin Wang, Yunchao Wei, Robert Jenssen, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks

Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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

Robert Jenssen, José Carlos Príncipe, Shujian Yu, 2020, IEEE transactions on pattern analysis and machine intelligence.

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

Michael Kampffmeyer, Robert Jenssen, Lorenzo Livi, 2018, ArXiv.

The Cauchy-Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels

Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, 2006, J. Frankl. Inst..

Deep generative models for reject inference in credit scoring

Michael Kampffmeyer, Kjersti Aas, Robert Jenssen, 2019, Knowl. Based Syst..

Using multi-anchors to identify patients suffering from multimorbidities

Robert Jenssen, I. Mora-Jiménez, Cristina Soguero-Ruíz, 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Self-Constructing Graph Convolutional Networks for Semantic Labeling

Michael C. Kampffmeyer, R. Jenssen, A. Salberg, 2020, IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium.

Reconsidering Representation Alignment for Multi-view Clustering

Michael Kampffmeyer, Robert Jenssen, Daniel J. Trosten, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Multiplex visibility graphs to investigate recurrent neural network dynamics

Robert Jenssen, Lorenzo Livi, Cesare Alippi, 2016, Scientific Reports.

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

Michael C. Kampffmeyer, R. Jenssen, Srishti Gautam, 2022, Medical Image Anal..

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Robert Jenssen, Davide Roverso, Van Nhan Nguyen, 2018, International Journal of Electrical Power & Energy Systems.

Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records

José Luis Rojo-Álvarez, Robert Jenssen, Stein Olav Skrøvseth, 2016, IEEE Journal of Biomedical and Health Informatics.

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

Daniel J. Trosten, Michael C. Kampffmeyer, R. Jenssen, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

The Deep Kernelized Autoencoder pdf

Michael Kampffmeyer, Robert Jenssen, Lorenzo Livi, 2018, Appl. Soft Comput..

Real-World Load Time Series

Robert Jenssen, Antonello Rizzi, Filippo Maria Bianchi, 2017 .

Dense Dilated Convolutions’ Merging Network for Land Cover Classification

Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, 2020, IEEE Transactions on Geoscience and Remote Sensing.

Urban Land Cover Classification With Missing Data Modalities Using Deep Convolutional Neural Networks

Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

Michael C. Kampffmeyer, R. Jenssen, A. Salberg, 2020, ArXiv.

The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space

Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, 2004, NIPS.

Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series

Robert Jenssen, Simone Scardapane, Filippo Maria Bianchi, 2018, IEEE Transactions on Neural Networks and Learning Systems.

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

Shujian Yu, R. Jenssen, Badong Chen, 2022, ArXiv.

Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function

R. Jenssen, M. Lubberink, J. Axelsson, 2021, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

Other Recurrent Neural Networks Models

Robert Jenssen, Antonello Rizzi, Filippo Maria Bianchi, 2017 .

Information Force Clustering Using Directed Trees

Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, 2003, EMMCVPR.

Information cut for clustering using a gradient descent approach

Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, 2007, Pattern Recognit..

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

Shujian Yu, R. Jenssen, Hongming Li, 2023, ArXiv.

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

Robert Jenssen, Karl Oyvind Mikalsen, Cristina Soguero-Ruiz, 2020, Explainable AI in Healthcare and Medicine.

Learning representations of multivariate time series with missing data

Michael Kampffmeyer, Robert Jenssen, Lorenzo Livi, 2019, Pattern Recognit..

The time series cluster kernel

Robert Jenssen, Filippo Maria Bianchi, Cristina Soguero-Ruíz, 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data pdf

Robert Jenssen, Filippo Maria Bianchi, Cristina Soguero-Ruíz, 2017, Pattern Recognit..

Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series

Karl Øyvind Mikalsen, Michael C. Kampffmeyer, R. Jenssen, 2020, IEEE Journal of Biomedical and Health Informatics.

Deep Image Clustering with Tensor Kernels and Unsupervised Companion Objectives pdf

Robert Jenssen, Michael C. Kampffmeyer, Daniel J. Trosten, 2019, ArXiv.

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

Karl Øyvind Mikalsen, Michael C. Kampffmeyer, R. Jenssen, 2022, Pattern Recognit. Lett..

Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images

Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, 2021 .

Deep Divergence-Based Approach to Clustering

Michael Kampffmeyer, Robert Jenssen, Lorenzo Livi, 2019, Neural Networks.

SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks

Michael Kampffmeyer, Robert Jenssen, Van Nhan Nguyen, 2020, ECCV.

Towards Applicability: A Comparative Study on Non-Intrusive Load Monitoring Algorithms*

Robert Jenssen, Filippo Maria Bianchi, Stian Normann Anfinsen, 2021, 2021 IEEE International Conference on Consumer Electronics (ICCE).

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

Michael Kampffmeyer, Robert Jenssen, José Carlos Príncipe, 2019, ArXiv.

Temporal overdrive recurrent neural network

Michael Kampffmeyer, Robert Jenssen, Filippo Maria Bianchi, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

A new information theoretic analysis of sum-of-squared-error kernel clustering

Robert Jenssen, Torbjørn Eltoft, R. Jenssen, 2008, Neurocomputing.

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

Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, 2019, 2019 Joint Urban Remote Sensing Event (JURSE).

Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration

Robert Jenssen, Jose C. Principe, Shujian Yu, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Deep Kernelized Autoencoders pdf

Michael Kampffmeyer, Robert Jenssen, Lorenzo Livi, 2017, SCIA.

ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement.

Michael C. Kampffmeyer, R. Jenssen, Srishti Gautam, 2023, Medical Image Analysis.

Semi-supervised target classification in multi-frequency echosounder data

Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, 2021, ICES Journal of Marine Science.

Multivariate Extension of Matrix-Based Rényi's $\alpha$α-Order Entropy Functional

Robert Jenssen, Jose C. Principe, Shujian Yu, 2018, IEEE Trans. Pattern Anal. Mach. Intell..

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

Daniel J. Trosten, Michael C. Kampffmeyer, R. Jenssen, 2023, Computer Vision and Pattern Recognition.

Recurrent Neural Network Architectures

Robert Jenssen, Antonello Rizzi, Filippo Maria Bianchi, 2017 .

Bidirectional deep-readout echo state networks

Robert Jenssen, Simone Scardapane, Filippo Maria Bianchi, 2017, ESANN.

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, 2019, Expert Syst. Appl..

Kernel Entropy Component Analysis

Robert Jenssen, R. Jenssen, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Optimizing the Cauchy-Schwarz PDF Distance for Information Theoretic, Non-parametric Clustering

Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, 2005, EMMCVPR.

Proceedings of the 16th Scandinavian Conference on Image Analysis

Robert Jenssen, Arnt-Børre Salberg, Jon Yngve Hardeberg, 2009, Scandinavian Conference on Image Analysis.

The Kernelized Taylor Diagram

Karl Øyvind Mikalsen, Michael C. Kampffmeyer, R. Jenssen, 2022, NAIS.

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

Robert Jenssen, Cristina Soguero-Ruíz, Karl Øyvind Mikalsen, 2021, ArXiv.

Machine learning derived input-function in a dynamic 18F-FDG PET study of mice

Robert Jenssen, Jan Axelsson, Samuel Kuttner, 2020, Biomedical physics & engineering express.

A generic unfolding algorithm for manifolds estimated by local linear approximations

Robert Jenssen, Deniz Erdogmus, Jonas Nordhaug Myhre, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Density ridge manifold traversal

Michael Kampffmeyer, Robert Jenssen, Jonas Nordhaug Myhre, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Recurrent Deep Divergence-based Clustering for Simultaneous Feature Learning and Clustering of Variable Length Time Series

Michael Kampffmeyer, Robert Jenssen, Andreas Storvik Strauman, 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Information theoretic clustering using a k-nearest neighbors approach

Robert Jenssen, Vidar Vikjord, R. Jenssen, 2014, Pattern Recognit..

A new information theoretic clustering algorithm using k-nn

Robert Jenssen, Vidar Vikjord, R. Jenssen, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Using anchors from free text in electronic health records to diagnose postoperative delirium

Karl Øyvind Mikalsen, F. Godtliebsen, R. Jenssen, 2017, Comput. Methods Programs Biomed..

Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods

F. Godtliebsen, R. Jenssen, J. Rojo-álvarez, 2016, J. Biomed. Informatics.

Mean Shift Spectral Clustering using Kernel Entropy Component Analysis

Robert Jenssen, Jorgen A. Agersborg, R. Jenssen, 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy

Michael C. Kampffmeyer, J. Príncipe, Shujian Yu, 2023, Entropy.

Multivariate Extension of Matrix-Based Rényi's

$\alpha$

-Order Entropy Functional

J. Príncipe, Shujian Yu, R. Jenssen, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Selective Imputation for Multivariate Time Series Datasets With Missing Values

Shujian Yu, R. Jenssen, Karl Oyvind Mikalsen, 2023, IEEE Transactions on Knowledge and Data Engineering.

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

Michael Kampffmeyer, Robert Jenssen, Filippo Maria Bianchi, 2017, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

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

Michael C. Kampffmeyer, R. Jenssen, Kristoffer Wickstrøm, 2018, Medical Image Anal..

Pattern Recognition

Jesús Ariel Carrasco-Ochoa, Ching Y. Suen, José Francisco Martínez-Trinidad, 2014, Lecture Notes in Computer Science.

Land-cover classification of partly missing data using support vector machines

Robert Jenssen, Arnt-Borre Salberg, R. Jenssen, 2012 .

Consensus Clustering Using kNN Mode Seeking

Robert Jenssen, Karl Øyvind Mikalsen, Jonas Nordhaug Myhre, 2015, SCIA.

Indefinite Parzen Window for Spectral Clustering

R. Jenssen, R. Jenssen, 2007, 2007 IEEE Workshop on Machine Learning for Signal Processing.

INDEFINITEPARZENWINDOW FOR SPECTRALCLUSTERING

R. Jenssen, 2007 .

Generating customer's credit behavior with deep generative models

Michael C. Kampffmeyer, R. Jenssen, K. Aas, 2022, Knowl. Based Syst..

Kernel Entropy Component Analysis Pre-images for Pattern Denoising

Robert Jenssen, Ola Storås, R. Jenssen, 2009, SCIA.

Some Equivalences between Kernel Methods and Information Theoretic Methods

Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, 2006, J. VLSI Signal Process..

The Laplacian spectral classifier

Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..