21 citations · 28 across the 8 of their papers we have counts for
3 papers · 1 filter
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
Francesco Cagnetta, Leonardo Petrini, Umberto M. Tomasini +2
Deep learning algorithms demonstrate a surprising ability to learn high-dimensional tasks from limited examples. This is commonly attributed to the depth of neural networks, enabli…
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…
Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data
Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart
Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has…