35 citations · 106 across the 15 of their papers we have counts for
3 papers · 1 filter
Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers
Federico Bassetti, Marco Gherardi, Alessandro Ingrosso +2
Deep linear networks have been extensively studied, as they provide simplified models of deep learning. However, little is known in the case of finite-width architectures with mult…
Restoring balance: principled under/oversampling of data for optimal classification
Emanuele Loffredo, Mauro Pastore, Simona Cocco +1
Class imbalance in real-world data poses a common bottleneck for machine learning tasks, since achieving good generalization on under-represented examples is often challenging. Mit…
Random features and polynomial rules
Fabián Aguirre-López, Silvio Franz, Mauro Pastore
Random features models play a distinguished role in the theory of deep learning, describing the behavior of neural networks close to their infinite-width limit. In this work, we pr…