Robert Jenssen

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Michael Christian KampffmeyerProfessor in Machine Learning, UiT the Arctic University of Norway, TromsøAdresă de e-mail confirmată pe post.uit.no
jose principeprofessor of ECE, U. of FloridaAdresă de e-mail confirmată pe cnel.ufl.edu
Arnt SalbergChief research scientist at Norwegian Computing CenterAdresă de e-mail confirmată pe nr.no
Karl Øyvind MikalsenUniversity Hospital of North Norway & UiT The Arctic University of Norway, TromsøAdresă de e-mail confirmată pe uit.no
Deniz ErdogmusProfessor of ECE, Northeastern UniversityAdresă de e-mail confirmată pe ece.neu.edu
Cristina Soguero RuizProfessor URJCAdresă de e-mail confirmată pe urjc.es
Gustau Camps-VallsProfessor. Image Processing Lab (IPL). Universitat de ValènciaAdresă de e-mail confirmată pe uv.es
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyAdresă de e-mail confirmată pe tu-berlin.de
Soeren SonnenburgMachine Learning ResearcherAdresă de e-mail confirmată pe tomtom.com
Marius KloftProfessor, RPTU Kaiserslautern-LandauAdresă de e-mail confirmată pe cs.uni-kl.de
Mark GirolamiUniversity of CambridgeAdresă de e-mail confirmată pe cam.ac.uk
Jianwu XuAdresă de e-mail confirmată pe alumni.ufl.edu
Oscar Fontenla-RomeroFull Professor of Computer Science. University of A Coruña (Spain)Adresă de e-mail confirmată pe udc.es
Kenneth E. Hild IISoftware Engineer, Lab126

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Robert Jenssen

Visual Intelligence, UiT The Arctic University of Norway & Norw. Comp. Center & P1 Centre AI, UCPH

Adresă de e-mail confirmată pe uit.no - Pagina de pornire

Machine learning information theoretic learning kernel methods deep learning health data analytics


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Information theoretic learning: Renyi's entropy and kernel perspectives JC Principe Springer Science & Business Media, 2010	1965	2010
Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks M Kampffmeyer, AB Salberg, R Jenssen Proceedings of the IEEE conference on computer vision and pattern …, 2016	725	2016
Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning R Jenssen, D Roverso International Journal of Electrical Power & Energy Systems 99, 107-120, 2018	522	2018
Kernel entropy component analysis R Jenssen IEEE transactions on pattern analysis and machine intelligence 32 (5), 847-860, 2009	400	2009
Recurrent neural networks for short-term load forecasting: an overview and comparative analysis FM Bianchi, E Maiorino, MC Kampffmeyer, A Rizzi, R Jenssen Springer, 2017	288	2017
An overview and comparative analysis of recurrent neural networks for short term load forecasting FM Bianchi, E Maiorino, MC Kampffmeyer, A Rizzi, R Jenssen arXiv preprint arXiv:1705.04378, 2017	270	2017
Reservoir computing approaches for representation and classification of multivariate time series FM Bianchi, S Scardapane, S Løkse, R Jenssen IEEE transactions on neural networks and learning systems 32 (5), 2169-2179, 2020	208	2020
Reconsidering representation alignment for multi-view clustering DJ Trosten, S Lokse, R Jenssen, M Kampffmeyer Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021	206	2021
Intelligent monitoring and inspection of power line components powered by UAVs and deep learning N Nguyen, R Jenssen, D Roverso IEEE Power and energy technology systems journal 6 (1), 11-21, 2019	198	2019
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps K Wickstrøm, M Kampffmeyer, R Jenssen Medical image analysis 60, 101619, 2020	167	2020
The Cauchy–Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels R Jenssen, JC Principe, D Erdogmus, T Eltoft Journal of the Franklin Institute 343 (6), 614-629, 2006	158	2006
Clustering using Renyi's entropy R Jenssen, KE Hild, D Erdogmus, JC Principe, T Eltoft Proceedings of the International Joint Conference on Neural Networks, 2003 …, 2003	148	2003
Dense dilated convolutions’ merging network for land cover classification Q Liu, M Kampffmeyer, R Jenssen, AB Salberg IEEE Transactions on Geoscience and Remote Sensing 58 (9), 6309-6320, 2020	135	2020
Deep image translation with an affinity-based change prior for unsupervised multimodal change detection LT Luppino, M Kampffmeyer, FM Bianchi, G Moser, SB Serpico, ... IEEE Transactions on Geoscience and Remote Sensing 60, 1-22, 2021	130	2021
Code-aligned autoencoders for unsupervised change detection in multimodal remote sensing images LT Luppino, MA Hansen, M Kampffmeyer, FM Bianchi, G Moser, ... IEEE Transactions on Neural Networks and Learning Systems 35 (1), 60-72, 2022	122	2022
Time series cluster kernel for learning similarities between multivariate time series with missing data KØ Mikalsen, FM Bianchi, C Soguero-Ruiz, R Jenssen Pattern Recognition 76, 569-581, 2018	121	2018
Mixing up contrastive learning: Self-supervised representation learning for time series K Wickstrøm, M Kampffmeyer, KØ Mikalsen, R Jenssen Pattern Recognition Letters 155, 54-61, 2022	114	2022
Spectral clustering of polarimetric SAR data with Wishart-derived distance measures SN Anfinsen, R Jenssen, T Eltoft Proc. POLinSAR 7, 1-9, 2007	114	2007
Multivariate Extension of Matrix-Based Rényi's -Order Entropy Functional S Yu, LGS Giraldo, R Jenssen, JC Principe IEEE transactions on pattern analysis and machine intelligence 42 (11), 2960 …, 2019	110	2019
Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels S Hansen, S Gautam, R Jenssen, M Kampffmeyer Medical Image Analysis 78, 102385, 2022	106	2022

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