Information theoretic learning: Renyi's entropy and kernel perspectives JC Principe
Springer Science & Business Media, 2010
1902 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
687 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
478 2018 Kernel entropy component analysis R Jenssen
IEEE transactions on pattern analysis and machine intelligence 32 (5), 847-860, 2009
391 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
265 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
258 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
183 2020 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
174 2019 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
159 2021 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
152 2006 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
151 2020 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
146 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
127 2020 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
116 2018 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
114 2021 Spectral clustering of polarimetric SAR data with Wishart-derived distance measures SN Anfinsen, R Jenssen, T Eltoft
Proc. POLinSAR 7, 1-9, 2007
112 2007 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
99 2022 Multivariate Extension of Matrix-Based Rényi's S Yu, LGS Giraldo, R Jenssen, JC Principe
IEEE transactions on pattern analysis and machine intelligence 42 (11), 2960 …, 2019
99 2019 Support vector feature selection for early detection of anastomosis leakage from bag-of-words in electronic health records C Soguero-Ruiz, K Hindberg, JL Rojo-Alvarez, SO Skrøvseth, ...
IEEE journal of biomedical and health informatics 20 (5), 1404-1415, 2014
97 2014 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
94 2022 
Michael Christian KampffmeyerProfessor in Machine Learning, UiT the Arctic University of Norway, TromsøEmail verificata su post.uit.no
