4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2022
How deep convolutional neural networks lose spatial information with training
Umberto M. Tomasini, Leonardo Petrini, Francesco Cagnetta +1
A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representatio…
cs.LG2020★ 4 cited
Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training
Mario Geiger, Leonardo Petrini, Matthieu Wyart
Deep learning algorithms are responsible for a technological revolution in a variety of tasks including image recognition or Go playing. Yet, why they work is not understood. Ultim…