3 papers
cs.LG2025
Graph Counterfactual Explainable AI via Latent Space Traversal
Andreas Abildtrup Hansen, Paraskevas Pegios, Anna Calissano +1
Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these mo…
cs.LG2025
Interpreting Equivariant Representations
Andreas Abildtrup Hansen, Anna Calissano, Aasa Feragen
Latent representations are used extensively for downstream tasks, such as visualization, interpolation or feature extraction of deep learning models. Invariant and equivariant neur…
cs.LG2024
Counterfactual Explanations via Riemannian Latent Space Traversal
Paraskevas Pegios, Aasa Feragen, Andreas Abildtrup Hansen +1
The adoption of increasingly complex deep models has fueled an urgent need for insight into how these models make predictions. Counterfactual explanations form a powerful tool for…