3 papers
cs.LG2026
Measuring What Matters: A Unified Evaluation Framework for GNN Explainability
Francesco Paolo Nerini, Mirko Zaffaroni, Paolo Baracco +2
Graph eXplainable AI (G-XAI) is increasingly important for making Graph Neural Networks interpretable and accountable. While a growing number of explainers are available, choosing…
cs.CV2026
SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders
Enrico Cassano, Riccardo Renzulli, Marco Nurisso +3
Concept unlearning in diffusion models is hampered by feature splitting, where concepts are distributed across many latent features, making their removal challenging and computatio…
cs.CV2024
AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
Mirko Zaffaroni, Federico Signoretta, Marco Grangetto +1
Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. Deep generative models achieve top performan…