58 citations · 133 across the 9 of their papers we have counts for
4 papers · 1 filter
Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions
Fabiola Ricci, Lorenzo Bardone, Sebastian Goldt
Deep neural networks learn structured features from complex, non-Gaussian inputs, but the mechanisms behind this process remain poorly understood. Our work is motivated by the obse…
Sliding down the stairs: how correlated latent variables accelerate learning with neural networks
Lorenzo Bardone, Sebastian Goldt
Neural networks extract features from data using stochastic gradient descent (SGD). In particular, higher-order input cumulants (HOCs) are crucial for their performance. However, e…
Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks
Eszter Székely, Lorenzo Bardone, Federica Gerace +1
Neural networks excel at discovering statistical patterns in high-dimensional data sets. In practice, higher-order cumulants, which quantify the non-Gaussian correlations between t…
Redundant representations help generalization in wide neural networks
Diego Doimo, Aldo Glielmo, Sebastian Goldt +1
Deep neural networks (DNNs) defy the classical bias-variance trade-off: adding parameters to a DNN that interpolates its training data will typically improve its generalization per…