Main Conference

08:45-09:15 - Registration
09:15-09:30 - Opening
09:30-10:15 - Keynote: Mihaela van der Schaar (Chair: Robert Jenssen)
10:15-10:45 - Coffee break
10:45-11:45 - Oral session* (Chair: Ahcene Boubekki)
10:45 - PLM-AS: Pre-trained Language Models Augmented with Scanpaths for Sentiment Classification, Duo Yang, Center for Language Technology, University of Copenhagen
11:05 - Contradiction Detection in Financial Reports, Tobias Deußer, University of Bonn, Maren Pielka, Fraunhofer IAIS
11:25 - Multi-lingual agents through multi-headed neural networks, Jonathan David Thomas, University of Bristol
11:45-12:45 - Lunch
12:45-14:35 - Industry event (Program)
14:35-15:00 - Coffee break
15:00-17:00 - Diversity in AI (Program)
17:00-18:00 - Finger food and networking
18:30 - Cable car trip: Discover Northern Lights at Fjellheisen

09:00-09:45 - Keynote: Christian Igel (Chair: Sarina Thomas)
09:45-10:10 - Coffee break
10:10-11:30 - Oral session* (Chair: Arnt-Børre Salberg)
10:10 - Boundary Aware U-Net for Glacier Segmentation, Olac Fuentes, Department of Computer Science, The University of Texas at El Paso
10:30 - Improved Imagery Throughput via Cascaded Uncertainty Pruning on U-Net++, Zifu Wang, KU Leuven ESAT(PSI)
10:50 - Semi- and weak-supervised learning for Norwegian tree species detection, David Völgyes, Science and Technology AS
11:10 - Improving Wind Speed Uncertainty Forecasts Using Recurrent Neural Networks, Juri Backes, University of Applied Sciences Hamburg (HAW)
11:30-12:30 - Lunch
12:30-13:10 - Oral session* (Chair: Alba Ordonez)
12:30 - Explainability in subgraphs-enhanced Graph Neural Networks, Michele Guerra, UiT the Arctic University of Norway
12:50 - A comparison between Tsetlin machines and deep neural networks in the context of recommendation systems, Karl Audun Borgersen, University of Agder
13:10-14:30 - Poster session 1 & Coffee
14:30-16:10 - Oral session* (Chair: Filippo Bianchi)
14:30 - FastDTI: Drug-Target Interaction Prediction using Multimodality and Transformers, Maryam Tavakol, Eindhoven University of Technology.
14:50 - Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation, Nicklas Boserup, Department of Computer Science, University of Copenhagen
15:10 - Signal and Visual Approaches for Parkinson's Disease Detection from Spiral Drawings, Cristina Luna-Jiménez, Universidad Politécnica de Madrid
15:30 - Reducing Annotator's Burden: Cross-Pseudo Supervision for Brain Tumor Segmentation, Lidia Luque, Center for Computational Radiology and Artificial Intelligence (CRAI), Oslo University Hospital
15:50 - Contrastive learning for unsupervised medical image clustering and reconstruction, Matteo Ferrante, University of Rome, Tor Vergata
19:00 - Conference dinner at Scandic Ishavshotel (Google Maps)

09:00-09:45 - Keynote: Polina Golland (Chair: Michael Kampffmeyer)
09:45-10:10 - Coffee break
10:10-11:30 - Oral session* (Chair: Signe Riemer-Sørensen)
10:10 - RIDDLE: Rule Induction with Deep Learning, Cosimo Persia, University of Bergen
10:30 - Questionable Practices in Methodological Deep Learning Research, Daniel J. Trosten, UiT Machine Learning Group
10:50 - Nearest Unitary and Toeplitz matrix techniques for adaptation of Deep Learning models in photonic FPGA, Georgios Agrafiotis, Centre for Research and Technology Hellas
11:10 - Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary, Ketil Malde, Institute of Marine Research and Department of Informatics, University of Bergen
11:30-12:30 - Lunch
12:30-13:10 - Oral session* (Chair: Narada Dilp Warakagoda)
12:30 - Using Mask R-CNN for Underwater Fish Instance Segmentation as Novel Objects: A Proof of Concept, I-Hao Chen, NORCE
12:50 - A contrastive learning approach for individual re-identification in a wild fish population, Ørjan Langøy Olsen, University of Agder
13:10-14:00 - NORA panel discussion
14:00-15:20 - Poster session 2 & Coffee
15:20-15:40 - NORA competition
15:40-17:00 - Oral session* (Chair: Georgios Leontidis)
15:40 - Evaluating current state of monocular 3D pose models for golf, Christian Keilstrup Ingwersen, TrackMan A/S + Technical University of Denmark
16:00 - Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics, Oskar Sjögren, Luleå University of Technology
16:20 - Multi-modal data generation with a deep metric variational autoencoder, Josefine Vilsbøll Sundgaard, Technical University of Denmark & Novo Nordisk A/S
16:40 - Interactive Scribble Segmentation, Mathias Michelsen Lowes, Jakob Lønborg Christensen and Bjørn Hansen, Technical University of Denmark
17:00 - Closing
17:30 - Visit at "The Science Centre of Northern Norway" (Google Maps)

#1 3D Convolutional Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI*, Alvaro Fernandez-Quilez, Stavanger Medical Imaging Laboratory, Department of Radiology, Stavanger University Hospital
#2 Simplifying Clustering with Graph Neural Networks*, Filippo Maria Bianchi, UiT the Arctic University of Norway and NORCE Norwegian Research Centre
#3 Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks*, Miquel Martí i Rabadán, Univrses AB, KTH Royal Institute of Technology
#4 Robin Pre-Training for the Deep Ritz Method*, Marius Zeinhofer, Simula Research Laboratory
#5 Solving Partial Differential Equations with Nonlinear Conservation Laws Using Graph Neural Networks*, Qing Li (University of Stavanger, Department of Energy and Petroleum, Group of Computational Engineering)
#6 Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort*, Bjørn-Jostein Singstad, Vestfold Hospital Trust and Simula Research Laboratory
#7 Automatic Postoperative Brain Tumor Segmentation with Limited Data using Transfer Learning and Triplet Attention*, Jingpeng Li, Division of Radiology and Nuclear Medicine, Oslo University Hospital and Department of Physics, University of Oslo
#8 A Neural Network Explainer in the Wavelet Domain, Stefan Kolek, Ludwig Maximilian University of Munich
#9 Attention-Based Deep Learning Methods for Predicting Gas Turbine Emissions, Rebecca Potts, University of Aberdeen
#10 Sparsifying Bayesian neural networks with latent binary variables and normalizing flows, Lars Skaaret-Lund, Norwegian University of Life Sciences
#11 Can a deep learning model trained to segment white matter hyperintensities (WMH) be used on patients that have combined WMH and stroke lesions?, Martin Soria Røvang, CRAI
#12 Uncertainty Quantification with Tsetlin Machine Using Ideas from Deep Learning, K. Darshana Abeyrathna, DNV
#13 ReSonet: Realistic Synthetic Social Networks with Graph Neural Networks, Alex Davies, University of Bristol
#14 Learning from multimodal reference atlases: from single cells to patients, Anastasia Litinetskaya, Helmholtz Center Munich
#15 Towards a More Data Efficient Sim2real Tactile Robotics Using Transfer Learning, Matt Clifford, University of Bristol
#16 Segmentation of post-operative glioblastoma, Ragnhild Holden Helland, SINTEF Digital / NTNU

*Full length paper that will be included in the proceedings