21 citations · 46 across the 12 of their papers we have counts for
3 papers · 1 filter
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…
What Can Be Learnt With Wide Convolutional Neural Networks?
Francesco Cagnetta, Alessandro Favero, Matthieu Wyart
Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harne…
Learning sparse features can lead to overfitting in neural networks
Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden +1
It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this…