10 citations · 16 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…