3 papers
cs.LG2026
A Law of Data Reconstruction for Random Features (and Beyond)
Leonardo Iurada, Simone Bombari, Tatiana Tommasi +1
Large-scale deep learning models are known to memorize parts of the training set. In machine learning theory, memorization is often framed as interpolation or label fitting, and cl…
cs.CV2025
A Second-Order Perspective on Pruning at Initialization and Knowledge Transfer
Leonardo Iurada, Beatrice Occhiena, Tatiana Tommasi
The widespread availability of pre-trained vision models has enabled numerous deep learning applications through their transferable representations. However, their computational an…
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…