149 citations · 545 across the 20 of their papers we have counts for
8 papers · 1 filter
The dimensionality of the Hopfield model
Cristopher Erazo, Santiago Acevedo, Alessandro Ingrosso
We use the Binary Intrinsic Dimension (BID), a geometrical measure designed for binary data, to analyze the Hopfield model, a paradigmatic spin system from statistical mechanics, m…
Generalization performance of narrow one-hidden layer networks in the teacher-student setting
Rodrigo Pérez Ortiz, Gibbs Nwemadji, Jean Barbier +4
Understanding the generalization properties of neural networks on simple input-output distributions is key to explaining their performance on real datasets. The classical teacher-s…
Statistical mechanics of transfer learning in fully-connected networks in the proportional limit
Alessandro Ingrosso, Rosalba Pacelli, Pietro Rotondo +1
Transfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related…
Data-driven emergence of convolutional structure in neural networks
Alessandro Ingrosso, Sebastian Goldt
Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate represen…
Training dynamically balanced excitatory-inhibitory networks
Alessandro Ingrosso, L. F. Abbott
The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networ…
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…
Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems
Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello +2
We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to efficiently sample solutions of random Constraint Satisfaction Problems (CSPs). First, we extend a recent result…
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses
Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello +2
We show that discrete synaptic weights can be efficiently used for learning in large scale neural systems, and lead to unanticipated computational performance. We focus on the repr…