Robert Jenssen

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robert.jenssen@uit.no

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Robert's P1 Publications

Assessing the Efficacy of Multi-task Learning in Mammographic Density Classification: A Study on Class Imbalance and Model Performance

Suaiba A. Salahuddin, Elisabeth Wetzer, Kristoffer Wickstrøm, Solveig Thrun, Michael Kampffmeyer, Robert Jenssen

Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders
Rogelio A. Mancisidor, Robert Jenssen, Shujian Yu, Michael Kampffmeyer
NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks
Bjørn L. Møller, Sepideh Amiri, Christian Igel, Kristoffer K. Wickstrøm, Robert Jenssen, Matthias Keicher, Mohammad F. Azampour, Nassir Navab, Bulat Ibragimov
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
Shujian Yu, Hongming Li, Sigurd Løkse, Robert Jenssen, José C. Príncipe
REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability
Kristoffer K. Wickstrøm, Thea Brüsch, Michael C. Kampffmeyer, Robert Jenssen
FreqRISE: Explaining time series using frequency masking
Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Tommy S. Alstrøm, Robert Jenssen
LSNetv2: Improving weakly supervised power line detection with bipartite matching
Duy K. Tran, Van N. Nguyen, Davide Roverso, Robert Jenssen, Michael Kampffmeyer
Leveraging tensor kernels to reduce objective function mismatch in deep clustering
Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer
Interrogating Sea Ice Predictability With Gradients
Harald L. Joakimsen, Iver Martinsen, Luigi T. Luppino, Andrew McDonald, Scott Hosking, Robert Jenssen
Instruction-guided deidentification with synthetic test cases for Norwegian clinical text
Jørgen A. Lund, Karl Ø. Mikalsen, Joel Burman, Ashenafi Z. Woldaregay, Robert Jenssen
Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks
Bjørn L. Møller, Christian Igel, Kristoffer K. Wickstrøm, Jon Sporring, Robert Jenssen, Bulat Ibragimov
DIB-X: Formulating Explainability Principles for a Self-Explainable Model Through Information Theoretic Learning
Changkyu Choi, Shujian Yu, Michael Kampffmeyer, Arnt-Børre Salberg, Nils O. Handegard, Robert Jenssen
Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice
Samuel Kuttner, Luigi T. Luppino, Laurence Convert, Otman Sarrhini, Roger Lecomte, Michael C. Kampffmeyer, Rune Sundset, Robert Jenssen
BrainIB: Interpretable Brain Network-Based Psychiatric Diagnosis With Graph Information Bottleneck
Kaizhong Zheng, Shujian Yu, Baojuan Li, Robert Jenssen, Badong Chen
Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data
Changkyu Choi, Michael Kampffmeyer, Nils O. Handegard, Arnt-Børre Salberg, Robert Jenssen
A Contextually Supported Abnormality Detector for Maritime Trajectories
Kristoffer V. Olesen, Ahcène Boubekki, Michael C. Kampffmeyer, Robert Jenssen, Anders N. Christensen, Sune Hørlück, Line H. Clemmensen
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings
Daniel J. Trosten, Rwiddhi Chakraborty , Sigurd Løksc, Kristoffer K. Wickstrøm, Robert Jenssen, Michael C. Kampffmeyer
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
Daniel J. Trosten, Sigurd Løkse, Robert Jenssen , Michael C. Kampffmeyer
Selective Imputation for Multivariate Time Series Datasets With Missing Values
Ane Blázquez-García, Kristoffer Wickstrøm, Shujian Yu, Karl Ø. Mikalsen, Ahcène Boubekki, Angel Conde, Usue Mori, Robert Jenssen, Jose A. Lozano

Robert's Programs

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People

P1 Professor

UiT The Arctic University of Norway

Visual Intelligence

Contact

Websites

Robert's P1 Publications

Assessing the Efficacy of Multi-task Learning in Mammographic Density Classification: A Study on Class Imbalance and Model Performance

Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders

NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks

The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making

REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability

FreqRISE: Explaining time series using frequency masking

LSNetv2: Improving weakly supervised power line detection with bipartite matching

Leveraging tensor kernels to reduce objective function mismatch in deep clustering

Interrogating Sea Ice Predictability With Gradients

Instruction-guided deidentification with synthetic test cases for Norwegian clinical text

Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks

DIB-X: Formulating Explainability Principles for a Self-Explainable Model Through Information Theoretic Learning

Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice

BrainIB: Interpretable Brain Network-Based Psychiatric Diagnosis With Graph Information Bottleneck

Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data

A Contextually Supported Abnormality Detector for Maritime Trajectories

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings

On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering

Selective Imputation for Multivariate Time Series Datasets With Missing Values

Robert's Programs

P1 Programs

Arctic AI

P1 Programs

AI for Marine Perception