3 papers
cs.CV2026
Towards Visually Explaining Statistical Tests with Applications in Biomedical Imaging
Masoumeh Javanbakhat, Piotr Komorowski, Dilyara Bareeva +3
Deep neural two-sample tests have recently shown strong power for detecting distributional differences between groups, yet their black-box nature limits interpretability and practi…
stat.ML2026
Concept activation vectors: a unifying view and adversarial attacks
Ekkehard Schnoor, Malik Tiomoko, Jawher Said +2
Concept Activation Vectors (CAVs) are a tool from explainable AI, offering a promising approach for understanding how human-understandable concepts are encoded in a model's latent…
cs.AI2025
Model Science: getting serious about verification, explanation and control of AI systems
Przemyslaw Biecek, Wojciech Samek
The growing adoption of foundation models calls for a paradigm shift from Data Science to Model Science. Unlike data-centric approaches, Model Science places the trained model at t…