3 papers
cs.LG2025
Efficient Model Editing with Task-Localized Sparse Fine-tuning
Leonardo Iurada, Marco Ciccone, Tatiana Tommasi
Task arithmetic has emerged as a promising approach for editing models by representing task-specific knowledge as composable task vectors. However, existing methods rely on network…
cs.CV2024
Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning
Leonardo Iurada, Marco Ciccone, Tatiana Tommasi
Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on th…
cs.CV2023
Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics
Leonardo Iurada, Silvia Bucci, Timothy M. Hospedales +1
Deep learning-based recognition systems are deployed at scale for several real-world applications that inevitably involve our social life. Although being of great support when maki…