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

Principle of Relevant Information for Graph Sparsification

May 20, 2022
By
Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen and Jose C. Principe

Using Machine Learning to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images

February 14, 2022
By
Nikita Shvetsov, Morten Grønnesby, Edvard Pedersen, Kajsa Møllersen, Lill-Tove Rasmussen Busund, Ruth Schwienbacher, Lars Ailo Bongo, Thomas K. Kilvaer

All publications

Machine Learning + Marine Science: Critical Role of Partnerships in Norway

By authors:

Nils Olav Handegard, Line Eikvil, Robert Jenssen, Michael Kampffmeyer, Arnt-Børre Salberg, and Ketil Malde

Published in:

Journal of Ocean Technology 2021

on

October 6, 2021

Semi-supervised target classification in multi-frequency echosounder data

By authors:

Changkyu Choi, Michael Kampffmeyer, Nils Olav Handegard, Arnt-Børre Salberg, Olav Brautaset, Line Eikvil, Robert Jenssen

Published in:

ICES Journal of Marine Science, Volume 78, Issue 7, October 2021, Pages 2615–2627

on

August 12, 2021

Joint optimization of an autoencoder for clustering and embedding

By authors:

Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen

Published in:

Machine Learning (2021)

on

June 21, 2021

Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images

By authors:

Qinghui Liu (Brian), Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg

Published in:

International Journal of Remote Sensing

on

June 16, 2021

Reconsidering Representation Alignment for Multi-view Clustering

By authors:

Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer

Published in:

CVPR 2021

on

March 13, 2021

Morphological and molecular breast cancer profiling through explainable machine learning

By authors:

Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Masaru Ishii, Albrecht Stenzinger, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller & Frederick Klauschen

Published in:

Nature Machine Intelligence volume 3, pages 355–366 (2021)

on

March 8, 2021

Pruning by explaining: A novel criterion for deep neural network prunin

By authors:

Yeom, Seul-Ki; Seegerer, Philipp; Lapuschkin, Sebastian; Binder, Alexander; Wiedemann, Simon; Müller, Klaus-Robert; Samek, Wojciech.

Published in:

Pattern Recognition

on

March 3, 2021

Measuring Dependence with Matrix‐Based Entropy Functional

By authors:

Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, Jose Principe

Published in:

AAAI 2021

on

January 25, 2021

Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps

By authors:

Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen

Published in:

Medical Image Analysis, Volume 60, February 2020, 101619

on

November 14, 2019

Other publications

annual reports