Carlo Lucibello | DeepAI

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Featured Co-authors

Matteo Negri
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Jennifer Chayes
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Marc Mézard
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Alessandro Ingrosso
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Federica Gerace
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∙ 04/28/2023

The Exponential Capacity of Dense Associative Memories

Recent generalizations of the Hopfield model of associative memories are...

0 Carlo Lucibello, et al. ∙

∙ 03/29/2023

The Hidden-Manifold Hopfield Model and a learning phase transition

The Hopfield model has a long-standing tradition in statistical physics,...

11 Matteo Negri, et al. ∙

∙ 02/06/2023

Noise-cleaning the precision matrix of fMRI time series

We present a comparison between various algorithms of inference of covar...

6 Miguel Ibáñez-Berganza, et al. ∙

∙ 10/27/2021

Deep learning via message passing algorithms based on belief propagation

Message-passing algorithms based on the Belief Propagation (BP) equation...

0 Carlo Lucibello, et al. ∙

∙ 12/11/2020

Reconstruction of Pairwise Interactions using Energy-Based Models

Pairwise models like the Ising model or the generalized Potts model have...

0 Christoph Feinauer, et al. ∙

∙ 06/14/2020

Entropic gradient descent algorithms and wide flat minima

The properties of flat minima in the empirical risk landscape of neural ...

7 Fabrizio Pittorino, et al. ∙

∙ 11/15/2019

Clustering of solutions in the symmetric binary perceptron

The geometrical features of the (non-convex) loss landscape of neural ne...

0 Carlo Baldassi, et al. ∙

∙ 05/13/2019

Generalized Approximate Survey Propagation for High-Dimensional Estimation

In Generalized Linear Estimation (GLE) problems, we seek to estimate a s...

0 Luca Saglietti, et al. ∙

∙ 02/01/2019

Signal propagation in continuous approximations of binary neural networks

The training of stochastic neural network models with binary (±1) weight...

0 George Stamatescu, et al. ∙

∙ 10/26/2017

On the role of synaptic stochasticity in training low-precision neural networks

Stochasticity and limited precision of synaptic weights in neural networ...

0 Carlo Baldassi, et al. ∙

∙ 05/20/2016

Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes

In artificial neural networks, learning from data is a computationally d...

0 Carlo Baldassi, et al. ∙

∙ 02/12/2016

Learning may need only a few bits of synaptic precision

Learning in neural networks poses peculiar challenges when using discret...

0 Carlo Baldassi, et al. ∙

∙ 11/18/2015

Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems

We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to effic...

0 Carlo Baldassi, et al. ∙

∙ 09/18/2015

Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses

We show that discrete synaptic weights can be efficiently used for learn...

0 Carlo Baldassi, et al. ∙

∙ 02/27/2014

Scaling hypothesis for the Euclidean bipartite matching problem

We propose a simple yet very predictive form, based on a Poisson's equat...

0 Sergio Caracciolo, et al. ∙

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