Scientific publications

At Visual Intelligence we work across our innovation areas to extract knowledge from large volumes of visual data more efficiently through automatic and intelligent data analysis. The work to address the core research challenges in deep learning: working with limited training data, utilizing context and dependencies, providing explainability, confidence and uncertainty, are important in all the innovation areas.

Featured blog posts

Interrogating Sea Ice Predictability With Gradients

February 14, 2024
By
Joakimsen, H. L., Martinsen I., Luppino, L. T., McDonald, A., Hosking, S., and Jenssen, R.

Merging clustering into deep supervised neural network

June 4, 2023
By
Durgesh Singh, Ahcéne Boubekki, Robert Jenssen, Michael C. Kampffmeyer

All publications

Interrogating Sea Ice Predictability With Gradients

By authors:

Joakimsen, H. L., Martinsen I., Luppino, L. T., McDonald, A., Hosking, S., and Jenssen, R.

Published in:

IEEE Geoscience and Remote Sensing Letters

on

February 14, 2024

Mixed Nash for Robust Federated Learning

By authors:

Xie, Wanyun; Pethick, Thomas; Ramezani-Kebrya, Ali; Cevher, Volkan

Published in:

Transactions on Machine Learning Research (02/2024)

on

February 4, 2024

On the Generalization of Stochastic Gradient Descent with Momentum

By authors:

Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang

Published in:

Journal of Machine Learning Research 25 (2024) 1-56

on

January 1, 2024

A Contextually Supported Abnormality Detector for Maritime Trajectories

By authors:

Olesen, Kristoffer Vinther; Boubekki, Ahcene; Kampffmeyer, Michael Christian; Jenssen, Robert; Christensen, Anders Nymark; Hørlück, Sune; Clemmensen, Line H. A

Published in:

Journal of Marine Science and Engineering (JMSE) 2023 ;Volum 11.(11)

on

October 31, 2023

View it like a radiologist: Shifted windows for deep learning augmentation of CT images

By authors:

Østmo, Eirik Agnalt; Wickstrøm, Kristoffer; Radiya, Keyur; Kampffmeyer, Michael; Jenssen, Robert.

Published in:

2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), Rome, Italy, 2023, pp. 1-6

on

October 23, 2023

On Measures of Uncertainty in Classification

By authors:

Chlaily, Saloua; Ratha, Debanshu; Lozou, Pigi; Marinoni, Andrea

Published in:

IEEE Transactions on Signal Processing 2023 ;Volum 71. s.3710-3725

on

October 12, 2023

SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering

By authors:

Bruno Souza; Marius Aasan; Helio Pedrini; Adıń Ramıŕez Rivera

Published in:

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France, 2023, pp. 4642-4647

on

October 2, 2023

Selective Imputation for Multivariate Time Series Datasets with Missing Values.

By authors:

Blazquez-Garcia, Ane; Wickstrøm, Kristoffer Knutsen; Yu, Shujian; Mikalsen, Karl Øyvind; Boubekki, Ahcene; Conde, Angel; Mori, Usue; Jenssen, Robert; Lozano, Jose A.

Published in:

Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9490-9501

on

September 1, 2023

Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation

By authors:

Tomasetti, Luca; Hansen, Stine; Khanmohammadi, Mahdieh; Engan, Kjersti; Høllesli, Liv Jorunn; Kurz, Kathinka Dæhli; Kampffmeyer, Michael Christian

Published in:

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp

on

September 1, 2023

Using Deep Learning Methods for Segmenting Polar Mesospheric Summer Echoes

By authors:

Domben, Erik Seip; Sharma, Puneet; Mann, Ingrid

Published in:

Remote Sensing 2023 ;Volum 15.(17) Suppl. 4291. s.1-23

on

August 31, 2023

Other publications

annual reports