4 papers
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…
Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale, Adrian V Dalca, Luca Saglietti +2
Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is th…
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…