research
∙
09/11/2023
Stochastic Gradient Descent-like relaxation is equivalent to Glauber dynamics in discrete optimization and inference problems
Is Stochastic Gradient Descent (SGD) substantially different from Glaube...
share
research
∙
08/08/2023
Parallel Learning by Multitasking Neural Networks
A modern challenge of Artificial Intelligence is learning multiple patte...
research
∙
05/10/2023
Phase transitions in the mini-batch size for sparse and dense neural networks
The use of mini-batches of data in training artificial neural networks i...
research
∙
10/10/2022
Multi-mode fiber reservoir computing overcomes shallow neural networks classifiers
In disordered photonics, one typically tries to characterize the optical...
research
∙
07/01/2022
The closest vector problem and the zero-temperature p-spin landscape for lossy compression
We consider a high-dimensional random constrained optimization problem i...
research
∙
06/27/2022
Cracking nuts with a sledgehammer: when modern graph neural networks do worse than classical greedy algorithms
The recent work “Combinatorial Optimization with Physics-Inspired Graph ...
research
∙
06/09/2022
A theory explaining the limits and performances of algorithms based on simulated annealing in solving sparse hard inference problems
The planted coloring problem is a prototypical inference problem for whi...
research
∙
11/26/2021
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization
Optimizing highly complex cost/energy functions over discrete variables ...
research
∙
05/29/2019
How to iron out rough landscapes and get optimal performances: Replicated Gradient Descent and its application to tensor PCA
In many high-dimensional estimation problems the main task consists in m...
research
∙
04/03/2019
Monte Carlo algorithms are very effective in finding the largest independent set in sparse random graphs
The effectiveness of stochastic algorithms based on Monte Carlo dynamics...
research
∙
11/05/2018
Biased landscapes for random Constraint Satisfaction Problems
The typical complexity of Constraint Satisfaction Problems (CSPs) can be...
research
∙
06/28/2018
Typology of phase transitions in Bayesian inference problems
Many inference problems, notably the stochastic block model (SBM) that g...
research
∙
02/16/2018
SpaRTA - Tracking across occlusions via global partitioning of 3D clouds of points
Any 3D tracking algorithm has to deal with occlusions: multiple targets ...
research
∙
10/15/2017
An improved Belief Propagation algorithm finds many Bethe states in the random field Ising model on random graphs
We first present an empirical study of the Belief Propagation (BP) algor...
research
∙
11/02/2016
Improving variational methods via pairwise linear response identities
Inference methods are often formulated as variational approximations: th...
research
∙
03/30/2016
Performance of a community detection algorithm based on semidefinite programming
The problem of detecting communities in a graph is maybe one the most st...
research
∙
08/20/2015
