
Reconstruction of Pairwise Interactions using EnergyBased Models
Pairwise models like the Ising model or the generalized Potts model have...
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Entropic gradient descent algorithms and wide flat minima
The properties of flat minima in the empirical risk landscape of neural ...
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Clustering of solutions in the symmetric binary perceptron
The geometrical features of the (nonconvex) loss landscape of neural ne...
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Generalized Approximate Survey Propagation for HighDimensional Estimation
In Generalized Linear Estimation (GLE) problems, we seek to estimate a s...
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Signal propagation in continuous approximations of binary neural networks
The training of stochastic neural network models with binary (±1) weight...
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On the role of synaptic stochasticity in training lowprecision neural networks
Stochasticity and limited precision of synaptic weights in neural networ...
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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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Learning may need only a few bits of synaptic precision
Learning in neural networks poses peculiar challenges when using discret...
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Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems
We introduce a novel Entropydriven Monte Carlo (EdMC) strategy to effic...
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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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Scaling hypothesis for the Euclidean bipartite matching problem
We propose a simple yet very predictive form, based on a Poisson's equat...
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Carlo Lucibello
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