| 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 |
| Training echo state networks with regularization through dimensionality reduction S Løkse, FM Bianchi, R Jenssen Cognitive Computation 9 (3), 364-378, 2017 | 79 | 2017 |
| Deep divergence-based approach to clustering M Kampffmeyer, S Løkse, FM Bianchi, L Livi, AB Salberg, R Jenssen Neural Networks 113, 91-101, 2019 | 75 | 2019 |
| Robust clustering using a kNN mode seeking ensemble JN Myhre, KØ Mikalsen, S Løkse, R Jenssen Pattern Recognition 76, 491-505, 2018 | 59 | 2018 |
| On the effects of self-supervision and contrastive alignment in deep multi-view clustering DJ Trosten, S Løkse, R Jenssen, MC Kampffmeyer Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 50 | 2023 |
| Bidirectional deep-readout echo state networks FM Bianchi, S Scardapane, S Løkse, R Jenssen arXiv preprint arXiv:1711.06509, 2017 | 45 | 2017 |
| Sen: A novel feature normalization dissimilarity measure for prototypical few-shot learning networks VN Nguyen, S Løkse, K Wickstrøm, M Kampffmeyer, D Roverso, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 42 | 2020 |
| 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 | 34 | 2019 |
| RELAX: Representation learning explainability KK Wickstrøm, DJ Trosten, S Løkse, A Boubekki, KØ Mikalsen, ... International Journal of Computer Vision 131 (6), 1584-1610, 2023 | 27 | 2023 |
| Deep kernelized autoencoders M Kampffmeyer, S Løkse, FM Bianchi, R Jenssen, L Livi Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway …, 2017 | 25 | 2017 |
| Hubs and hyperspheres: Reducing hubness and improving transductive few-shot learning with hyperspherical embeddings DJ Trosten, R Chakraborty, S Løkse, KK Wickstrøm, R Jenssen, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 23 | 2023 |
| The deep kernelized autoencoder M Kampffmeyer, S Løkse, FM Bianchi, R Jenssen, L Livi Applied Soft Computing 71, 816-825, 2018 | 19 | 2018 |
| The conditional cauchy-schwarz divergence with applications to time-series data and sequential decision making S Yu, H Li, S Løkse, R Jenssen, JC Príncipe arXiv preprint arXiv:2301.08970, 2023 | 12 | 2023 |
| Spectral Clustering Using PCKID – A Probabilistic Cluster Kernel for Incomplete Data S Løkse, FM Bianchi, AB Salberg, R Jenssen Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway …, 2017 | 9 | 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 | 8 | |
| Consensus clustering using knn mode seeking JN Myhre, KØ Mikalsen, S Løkse, R Jenssen Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark …, 2015 | 6 | 2015 |
| Cauchy-Schwarz Divergence Information Bottleneck for Regression S Yu, X Yi, S Løkse, R Jenssen, JC Principe International Conference on Learning Representations (ICLR), 2024 | 4 | 2024 |
| The Kernelized Taylor Diagram K Wickstrøm, JE Johnson, S Løkse, G Camps-Valls, KØ Mikalsen, ... Symposium of the Norwegian AI Society, 125-131, 2022 | 3 | 2022 |
| Leveraging tensor kernels to reduce objective function mismatch in deep clustering DJ Trosten, S Løkse, R Jenssen, M Kampffmeyer Pattern Recognition 149, 110229, 2024 | 2 | 2024 |
Robert JenssenVisual Intelligence, UiT The Arctic University of Norway & Norw. Comp. Center & P1 Centre AI, UCPHAdresă de e-mail confirmată pe uit.no
