22 citations · 42 across the 11 of their papers we have counts for
7 papers · 1 filter
Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks
Dario Bocchi, Theotime Regimbeau, Carlo Lucibello +2
We analyze the one-pass stochastic gradient descent dynamics of a two-layer neural network with quadratic activations in a teacher--student framework. In the high-dimensional regim…
The star-shaped space of solutions of the spherical negative perceptron
Brandon Livio Annesi, Clarissa Lauditi, Carlo Lucibello +4
Empirical studies on the landscape of neural networks have shown that low-energy configurations are often found in complex connected structures, where zero-energy paths between pai…
Large Deviations of Semi-supervised Learning in the Stochastic Block Model
Hugo Cui, Luca Saglietti, Lenka Zdeborová
In community detection on graphs, the semi-supervised learning problem entails inferring the ground-truth membership of each node in a graph, given the connectivity structure and a…
Large deviations for the perceptron model and consequences for active learning
Hugo Cui, Luca Saglietti, Lenka Zdeborová
Active learning is a branch of machine learning that deals with problems where unlabeled data is abundant yet obtaining labels is expensive. The learning algorithm has the possibil…
Generalized Approximate Survey Propagation for High-Dimensional Estimation
Luca Saglietti, Yue M. Lu, Carlo Lucibello
In Generalized Linear Estimation (GLE) problems, we seek to estimate a signal that is observed through a linear transform followed by a component-wise, possibly nonlinear and noisy…
From statistical inference to a differential learning rule for stochastic neural networks
Luca Saglietti, Federica Gerace, Alessandro Ingrosso +2
Stochastic neural networks are a prototypical computational device able to build a probabilistic representation of an ensemble of external stimuli. Building on the relationship bet…