10 citations · 16 across the 9 of their papers we have counts for
10 papers
Backdoor Channels Hidden in Latent Space: Extending Cryptographic Undetectability to Modern Neural Networks
Marte Eggen, Eirik Reiestad, Kristian Gjøsteen +1
Recent cryptographic results establish that neural networks can be backdoored such that no efficient algorithm can distinguish them from a clean model. These guarantees, however, h…
Integrating attention into explanation frameworks for language and vision transformers
Marte Eggen, Jacob Lysnæs-Larsen, Inga Strümke
The attention mechanism lies at the core of the transformer architecture, providing an interpretable model-internal signal that has motivated a growing interest in attention-based…
Towards Biomarker Discovery for Early Cerebral Palsy Detection: Evaluating Explanations Through Kinematic Perturbations
Kimji N. Pellano, Inga Strümke, Daniel Groos +3
Cerebral Palsy (CP) is a prevalent motor disability in children, for which early detection can significantly improve treatment outcomes. While skeleton-based Graph Convolutional Ne…
What Does Explainable AI Mean in Practice? Evaluative Requirements from a Longitudinal Clinical Case Study
Tor Sporsem, Stine Rasdal Finserås, Lars Adde +1
This paper reports a case study on how explainability requirements were elicited during the development of an AI system for predicting cerebral palsy (CP) risk in infants. Over 18…
Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition
Felix Tempel, Daniel Groos, Espen Alexander F. Ihlen +2
Explaining machine learning (ML) models using eXplainable AI (XAI) techniques has become essential to make them more transparent and trustworthy. This is especially important in hi…
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
Luis M. Lopez-Ramos, Florian Leiser, Aditya Rastogi +6
The joint implementation of federated learning (FL) and explainable artificial intelligence (XAI) could allow training models from distributed data and explaining their inner worki…
A transformer-based deep reinforcement learning approach to spatial navigation in a partially observable Morris Water Maze
Marte Eggen, Inga Strümke
Navigation is a fundamental cognitive skill extensively studied in neuroscientific experiments and has lately gained substantial interest in artificial intelligence research. Recre…
Lightweight Neural Architecture Search for Cerebral Palsy Detection
Felix Tempel, Espen Alexander F. Ihlen, Inga Strümke
The neurological condition known as cerebral palsy (CP) first manifests in infancy or early childhood and has a lifelong impact on motor coordination and body movement. CP is one o…
Lecture Notes in Probabilistic Diffusion Models
Inga Strümke, Helge Langseth
Diffusion models are loosely modelled based on non-equilibrium thermodynamics, where \textit{diffusion} refers to particles flowing from high-concentration regions towards low-conc…
Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models
Patrik Hammersborg, Inga Strümke
Neural network models are widely used in a variety of domains, often as black-box solutions, since they are not directly interpretable for humans. The field of explainable artifici…