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04/28/2023
The Exponential Capacity of Dense Associative Memories
Recent generalizations of the Hopfield model of associative memories are...
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03/29/2023
The Hidden-Manifold Hopfield Model and a learning phase transition
The Hopfield model has a long-standing tradition in statistical physics,...
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02/06/2023
Noise-cleaning the precision matrix of fMRI time series
We present a comparison between various algorithms of inference of covar...
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10/27/2021
Deep learning via message passing algorithms based on belief propagation
Message-passing algorithms based on the Belief Propagation (BP) equation...
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12/11/2020
Reconstruction of Pairwise Interactions using Energy-Based Models
Pairwise models like the Ising model or the generalized Potts model have...
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06/14/2020
Entropic gradient descent algorithms and wide flat minima
The properties of flat minima in the empirical risk landscape of neural ...
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11/15/2019
Clustering of solutions in the symmetric binary perceptron
The geometrical features of the (non-convex) loss landscape of neural ne...
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05/13/2019
Generalized Approximate Survey Propagation for High-Dimensional Estimation
In Generalized Linear Estimation (GLE) problems, we seek to estimate a s...
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02/01/2019
Signal propagation in continuous approximations of binary neural networks
The training of stochastic neural network models with binary (±1) weight...
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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...
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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...
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02/12/2016
Learning may need only a few bits of synaptic precision
Learning in neural networks poses peculiar challenges when using discret...
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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...
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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...
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02/27/2014
