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