35 citations · 360 across the 36 of their papers we have counts for
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Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalisation
Simone Ciceri, Lorenzo Cassani, Matteo Osella +3
To achieve near-zero training error in a classification problem, the layers of a feed-forward network have to disentangle the manifolds of data points with different labels, to fac…
Beyond the storage capacity: data driven satisfiability transition
Pietro Rotondo, Mauro Pastore, Marco Gherardi
Data structure has a dramatic impact on the properties of neural networks, yet its significance in the established theoretical frameworks is poorly understood. Here we compute the…
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…