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Sigurd Løkse
Sigurd Løkse
Research Scientist at NORCE Norwegian Research Centre
Verified email at norceresearch.no
Title
Cited by
Cited by
Year
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
1952020
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
1762021
Training echo state networks with regularization through dimensionality reduction
S Løkse, FM Bianchi, R Jenssen
Cognitive Computation 9, 364-378, 2017
752017
Deep divergence-based approach to clustering
M Kampffmeyer, S Løkse, FM Bianchi, L Livi, AB Salberg, R Jenssen
Neural Networks 113, 91-101, 2019
712019
Robust clustering using a kNN mode seeking ensemble
JN Myhre, KØ Mikalsen, S Løkse, R Jenssen
Pattern Recognition 76, 491-505, 2018
552018
Bidirectional deep-readout echo state networks
FM Bianchi, S Scardapane, S Løkse, R Jenssen
arXiv preprint arXiv:1711.06509, 2017
432017
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
412020
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
352019
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
332023
Deep kernelized autoencoders
M Kampffmeyer, S Løkse, FM Bianchi, R Jenssen, L Livi
Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway …, 2017
252017
RELAX: Representation learning explainability
KK Wickstrøm, DJ Trosten, S Løkse, A Boubekki, KØ Mikalsen, ...
International Journal of Computer Vision 131 (6), 1584-1610, 2023
202023
The deep kernelized autoencoder
M Kampffmeyer, S Løkse, FM Bianchi, R Jenssen, L Livi
Applied Soft Computing 71, 816-825, 2018
192018
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
182023
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
92017
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
82023
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
62015
The Kernelized Taylor Diagram
K Wickstrøm, JE Johnson, S Løkse, G Camps-Valls, KØ Mikalsen, ...
Symposium of the Norwegian AI Society, 125-131, 2022
32022
Leveraging tensor kernels to reduce objective function mismatch in deep clustering
DJ Trosten, S Løkse, R Jenssen, M Kampffmeyer
Pattern Recognition 149, 110229, 2024
22024
On the Role of Self-supervision in Deep Multi-view Clustering
DJ Trosten, S Løkse, R Jenssen, M Kampffmeyer
22022
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