1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Transient Fault Tolerant Semantic Segmentation for Autonomous Driving
Leonardo Iurada, Niccolò Cavagnero, Fernando Fernandes Dos Santos +3
Deep learning models are crucial for autonomous vehicle perception, but their reliability is challenged by algorithmic limitations and hardware faults. We address the latter by exa…
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…