| 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 | 68 | 2020 |
| Training echo state networks with regularization through dimensionality reduction S Løkse, FM Bianchi, R Jenssen Cognitive Computation 9 (3), 364-378, 2017 | 53 | 2017 |
| Robust clustering using a kNN mode seeking ensemble JN Myhre, KØ Mikalsen, S Løkse, R Jenssen Pattern Recognition 76, 491-505, 2018 | 38 | 2018 |
| Deep divergence-based approach to clustering M Kampffmeyer, S Løkse, FM Bianchi, L Livi, AB Salberg, R Jenssen Neural Networks 113, 91-101, 2019 | 28 | 2019 |
| Information plane analysis of deep neural networks via matrix-based Renyi's entropy and tensor kernels K Wickstrøm, S Løkse, M Kampffmeyer, S Yu, J Principe, R Jenssen arXiv preprint arXiv:1909.11396, 2019 | 24 | 2019 |
| Bidirectional deep-readout echo state networks FM Bianchi, S Scardapane, S Løkse, R Jenssen arXiv preprint arXiv:1711.06509, 2017 | 21 | 2017 |
| Deep kernelized autoencoders M Kampffmeyer, S Løkse, FM Bianchi, R Jenssen, L Livi Scandinavian conference on image analysis, 419-430, 2017 | 21 | 2017 |
| The deep kernelized autoencoder M Kampffmeyer, S Løkse, FM Bianchi, R Jenssen, L Livi Applied Soft Computing 71, 816-825, 2018 | 14 | 2018 |
| Sen: A novel feature normalization dissimilarity measure for prototypical few-shot learning networks VN Nguyen, S Løkse, K Wickstrøm, M Kampffmeyer, D Roverso, ... European Conference on Computer Vision, 118-134, 2020 | 13 | 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 | 12 | 2021 |
| Spectral Clustering Using PCKID – A Probabilistic Cluster Kernel for Incomplete Data S Løkse, FM Bianchi, AB Salberg, R Jenssen Scandinavian Conference on Image Analysis, 431-442, 2017 | 7 | 2017 |
| Information plane analysis of deep neural networks via matrix-based Renyi’s entropy and tensor kernels. arXiv 2019 K Wickstrøm, S Løkse, M Kampffmeyer, S Yu, J Principe, R Jenssen arXiv preprint arXiv:1909.11396, 0 | 7 | |
| Consensus clustering using knn mode seeking JN Myhre, KØ Mikalsen, S Løkse, R Jenssen Scandinavian Conference on Image Analysis, 175-186, 2015 | 6 | 2015 |
| Joint ranking and clustering based on Markov Chain transition probabilities learned from data S Løkse UiT Norges arktiske universitet, 2014 | 1 | 2014 |
| The Kernelized Taylor Diagram K Wickstrøm, JE Johnson, S Løkse, G Camps-Valls, KØ Mikalsen, ... arXiv preprint arXiv:2205.08864, 2022 | | 2022 |
| RELAX: Representation Learning Explainability KK Wickstrøm, DJ Trosten, S Løkse, KØ Mikalsen, MC Kampffmeyer, ... arXiv preprint arXiv:2112.10161, 2021 | | 2021 |
| Ranking Using Transition Probabilities Learned from Multi-Attribute Data S Lokse, R Jenssen 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | | 2018 |
| Bidirectional deep echo state networks FM Bianchi, S Scardapane, S Løkse, R Jenssen arXiv preprint arXiv:1711.06509, 2017 | | 2017 |
| Supplementary material: Reconsidering Representation Alignment for Multi-view Clustering DJ Trosten, S Løkse, R Jenssen, M Kampffmeyer | | |
Robert JenssenSFI Visual Intelligence. UiT The Arctic University of Norway & Norwegian Computing Center & UPCHVerifisert e-postadresse på uit.no
