| 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 | 105 | 2020 |
| Training echo state networks with regularization through dimensionality reduction S Løkse, FM Bianchi, R Jenssen Cognitive Computation 9, 364-378, 2017 | 62 | 2017 |
| Robust clustering using a kNN mode seeking ensemble JN Myhre, KØ Mikalsen, S Løkse, R Jenssen Pattern Recognition 76, 491-505, 2018 | 48 | 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 | 44 | 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 | 31 | 2021 |
| Bidirectional deep-readout echo state networks FM Bianchi, S Scardapane, S Løkse, R Jenssen arXiv preprint arXiv:1711.06509, 2017 | 31 | 2017 |
| 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 | 28 | 2019 |
| 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 | 26 | 2020 |
| Deep kernelized autoencoders M Kampffmeyer, S Løkse, FM Bianchi, R Jenssen, L Livi Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway …, 2017 | 23 | 2017 |
| The deep kernelized autoencoder M Kampffmeyer, S Løkse, FM Bianchi, R Jenssen, L Livi Applied Soft Computing 71, 816-825, 2018 | 16 | 2018 |
| 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 | 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 Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark …, 2015 | 6 | 2015 |
| Relax: Representation learning explainability KK Wickstrøm, DJ Trosten, S Løkse, A Boubekki, K Mikalsen, ... International Journal of Computer Vision, 1-27, 2023 | 2 | 2023 |
| Joint ranking and clustering based on Markov Chain transition probabilities learned from data S Løkse UiT Norges arktiske universitet, 2014 | 1 | 2014 |
| On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering DJ Trosten, S Løkse, R Jenssen, MC Kampffmeyer arXiv preprint arXiv:2303.09877, 2023 | | 2023 |
| Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings DJ Trosten, R Chakraborty, S Løkse, KK Wickstrøm, R Jenssen, ... arXiv preprint arXiv:2303.09352, 2023 | | 2023 |
| The Kernelized Taylor Diagram K Wickstrøm, JE Johnson, S Løkse, G Camps-Valls, KØ Mikalsen, ... Nordic Artificial Intelligence Research and Development: 4th Symposium of …, 2023 | | 2023 |
| 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 | | 2023 |
| Ranking Using Transition Probabilities Learned from Multi-Attribute Data S Lokse, R Jenssen 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | | 2018 |
Robert JenssenVisual Intelligence, UiT The Arctic University of Norway & Norw. Comp. Center & P1 Centre AI, UCPHVerified email at uit.no
