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Found 16 papers, 2 papers with code
Stochastic Gradient Descent-like relaxation is equivalent to Glauber dynamics in discrete optimization and inference problems
Is Stochastic Gradient Descent (SGD) substantially different from Glauber dynamics?
Parallel Learning by Multitasking Neural Networks
A modern challenge of Artificial Intelligence is learning multiple patterns at once (i. e. parallel learning).
Phase transitions in the mini-batch size for sparse and dense two-layer neural networks
This work presents a systematic attempt at understanding the role of the mini-batch size in training two-layer neural networks.
Multi-mode fiber reservoir computing overcomes shallow neural networks classifiers
In the field of disordered photonics, a common objective is to characterize optically opaque materials for controlling light delivery or performing imaging.
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set
In this comment, we show that a simple greedy algorithm, running in almost linear time, can find solutions for the MIS problem of much better quality than the GNN.
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization
In particular, for 90% of random 4-SAT instances we find solutions that are inaccessible for the best specialized deterministic algorithm known as Survey Propagation (SP) with an order of magnitude improvement in the quality of solutions for the hardest 10% instances.
Entropic barriers as a reason for hardness in both classical and quantum algorithms
We study both classical and quantum algorithms to solve a hard optimization problem, namely 3-XORSAT on 3-regular random graphs.
Disordered Systems and Neural Networks Statistical Mechanics Quantum Physics
Inferring the particle-wise dynamics of amorphous solids from the local structure at the jamming point
Jamming is a phenomenon shared by a wide variety of systems, such as granular materials, foams, and glasses in their high density regime.
Statistical Mechanics Disordered Systems and Neural Networks
How to iron out rough landscapes and get optimal performances: Averaged Gradient Descent and its application to tensor PCA
In many high-dimensional estimation problems the main task consists in minimizing a cost function, which is often strongly non-convex when scanned in the space of parameters to be estimated.
A fast and accurate algorithm for inferring sparse Ising models via parameters activation to maximize the pseudo-likelihood
We propose a new algorithm to learn the network of the interactions of pairwise Ising models.
Disordered Systems and Neural Networks
SpaRTA - Tracking across occlusions via global partitioning of 3D clouds of points
Any 3D tracking algorithm has to deal with occlusions: multiple targets get so close to each other that the loss of their identities becomes likely.
Improving variational methods via pairwise linear response identities
Inference methods are often formulated as variational approximations: these approximations allow easy evaluation of statistics by marginalization or linear response, but these estimates can be inconsistent.
Performance of a community detection algorithm based on semidefinite programming
In this paper we study in detail several practical aspects of this new algorithm based on semidefinite programming for the detection of the planted partition.
The backtracking survey propagation algorithm for solving random K-SAT problems
Discrete combinatorial optimization has a central role in many scientific disciplines, however, for hard problems we lack linear time algorithms that would allow us to solve very large instances.
On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms
We introduce a version of the cavity method for diluted mean-field spin models that allows the computation of thermodynamic quantities similar to the Franz-Parisi quenched potential in sparse random graph models.
Disordered Systems and Neural Networks Statistical Mechanics Discrete Mathematics
Solving Constraint Satisfaction Problems through Belief Propagation-guided decimation
Message passing algorithms have proved surprisingly successful in solving hard constraint satisfaction problems on sparse random graphs.
