35 citations · 360 across the 36 of their papers we have counts for
4 papers · 1 filter
Intrinsic dimension estimation for locally undersampled data
Vittorio Erba, Marco Gherardi, Pietro Rotondo
High-dimensional data are ubiquitous in contemporary science and finding methods to compress them is one of the primary goals of machine learning. Given a dataset lying in a high-d…
Counting the learnable functions of structured data
Pietro Rotondo, Marco Cosentino Lagomarsino, Marco Gherardi
Cover's function counting theorem is a milestone in the theory of artificial neural networks. It provides an answer to the fundamental question of determining how many binary assig…
Generalization from correlated sets of patterns in the perceptron
Francesco Borra, Marco Cosentino Lagomarsino, Pietro Rotondo +1
Generalization is a central aspect of learning theory. Here, we propose a framework that explores an auxiliary task-dependent notion of generalization, and attempts to quantitative…
Efficiency and shrinking in evolving networks
Arianna Bottinelli, Marco Gherardi, Marc Barthelemy
Characterizing the spatio-temporal evolution of networks is a central topic in many disciplines. While network expansion has been studied thoroughly, less is known about how empiri…