1.6k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Politecnico di TorinoIT83 papers
- Collegio Carlo AlbertoIT36 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di TorinoIT27 papers
- Sapienza University of RomeIT21 papers
- Centre National de la Recherche ScientifiqueFR19 papers
- Istituto di NanotecnologiaIT19 papers
- National Research CouncilLK16 papers
- Sorbonne UniversitéFR14 papers
- Biologie Computationnelle, Quantitative et SynthétiqueFR12 papers
- École Normale Supérieure - PSLFR10 papers
- Institut de Biologie Paris-SeineFR9 papers
- Istituto Nazionale di Fisica NucleareIT9 papers
23 papers · 1 filter
Evidence of Replica Symmetry Breaking under the Nishimori conditions in epidemic inference on graphs
Alfredo Braunstein, Louise Budzynski, Matteo Mariani +1
In Bayesian inference, computing the posterior distribution from the data is typically a non-trivial problem, which usually requires approximations such as mean-field approaches or…
Small-Coupling Dynamic Cavity: a Bayesian mean-field framework for epidemic inference
Alfredo Braunstein, Giovanni Catania, Luca Dall'Asta +3
We present the Small-Coupling Dynamic Cavity (SCDC) method, a novel generalized mean-field approximation for epidemic inference and risk assessment within a fully Bayesian framewor…
The closest vector problem and the zero-temperature p-spin landscape for lossy compression
Alfredo Braunstein, Louise Budzynski, Stefano Crotti +1
We consider a high-dimensional random constrained optimization problem in which a set of binary variables is subjected to a linear system of equations. The cost function is a simpl…
Generalized Approximate Survey Propagation for High-Dimensional Estimation
Luca Saglietti, Yue M. Lu, Carlo Lucibello
In Generalized Linear Estimation (GLE) problems, we seek to estimate a signal that is observed through a linear transform followed by a component-wise, possibly nonlinear and noisy…
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…
The Random Fractional Matching Problem
Carlo Lucibello, Enrico M. Malatesta, Giorgio Parisi +1
We consider two formulations of the random-link fractional matching problem, a relaxed version of the more standard random-link (integer) matching problem. In one formulation, we a…
On the role of synaptic stochasticity in training low-precision neural networks
Carlo Baldassi, Federica Gerace, Hilbert J. Kappen +4
Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes. Here we show that a…
Loop expansion around the Bethe approximation through the -layer construction
Ada Altieri, Maria Chiara Angelini, Carlo Lucibello +3
For every physical model defined on a generic graph or factor graph, the Bethe -layer construction allows building a different model for which the Bethe approximation is exact i…
Numerical estimate of the Kardar Parisi Zhang universality class in (2 + 1) dimensions
Andrea Pagnani, Giorgio Parisi
We study the Restricted Solid on Solid model for surface growth in spatial dimension by means of a multi-surface coding technique that allows to produce a large number of sam…
One-loop diagrams in the Random Euclidean Matching Problem
Carlo Lucibello, Giorgio Parisi, Gabriele Sicuro
The matching problem is a notorious combinatorial optimization problem that has attracted for many years the attention of the statistical physics community. Here we analyze the Euc…
Inverse problem for multi-body interaction of nonlinear waves
Alessia Marruzzo, Payal Tyagi, Fabrizio Antenucci +2
The inverse problem is studied in multi-body systems with nonlinear dynamics representing, e.g., phase-locked wave systems, standard multimode and random lasers. Using a general mo…
Regularization and decimation pseudolikelihood approaches to statistical inference in -spin models
Payal Tyagi, Alessia Marruzzo, Andrea Pagnani +2
We implement a pseudolikelyhood approach with l2-regularization as well as the recently introduced pseudolikelihood with decimation procedure to the inverse problem in continuous s…
Learning may need only a few bits of synaptic precision
Carlo Baldassi, Federica Gerace, Carlo Lucibello +2
Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states. The choice of discrete synapses is motivated by biological reas…
The large deviations of the whitening process in random constraint satisfaction problems
Alfredo Braunstein, Luca Dall'Asta, Guilhem Semerjian +1
Random constraint satisfaction problems undergo several phase transitions as the ratio between the number of constraints and the number of variables is varied. When this ratio exce…
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…
A Max-Sum algorithm for training discrete neural networks
Carlo Baldassi, Alfredo Braunstein
We present an efficient learning algorithm for the problem of training neural networks with discrete synapses, a well-known hard (NP-complete) discrete optimization problem. The al…
The edge-disjoint path problem on random graphs by message-passing
Fabrizio Altarelli, Alfredo Braunstein, Luca Dall'Asta +2
We present a message-passing algorithm to solve the edge disjoint path problem (EDP) on graphs incorporating under a unique framework both traffic optimization and path length mini…
Theory and learning protocols for the material tempotron model
Carlo Baldassi, Alfredo Braunstein, Riccardo Zecchina
Neural networks are able to extract information from the timing of spikes. Here we provide new results on the behavior of the simplest neuronal model which is able to decode inform…
Stability of the replica symmetric solution in diluted perceptron learning
Alejandro Lage-Castellanos, Andrea Pagnani, Gretel Quintero Angulo
We study the role played by the dilution in the average behavior of a perceptron model with continuous coupling with the replica method. We analyze the stability of the replica sym…
Multi-surface coding simulations of the restricted solid-on-solid model in four dimensions
Andrea Pagnani, Giorgio Parisi
We study the Restricted Solid on Solid (RSOS) model for surface growth in spatial dimension d=4 by means of a multi-surface coding technique that allows to analyze samples to analy…
Optimizing spread dynamics on graphs by message passing
Fabrizio Altarelli, Alfredo Braunstein, Luca Dall'Asta +1
Cascade processes are responsible for many important phenomena in natural and social sciences. Simple models of irreversible dynamics on graphs, in which nodes activate depending o…
Inference and learning in sparse systems with multiple states
A. Braunstein, A. Ramezanpour, R. Zecchina +1
We discuss how inference can be performed when data are sampled from the non-ergodic phase of systems with multiple attractors. We take as model system the finite connectivity Hopf…