Cover Image

Inferring effective couplings with Restricted Boltzmann Machines

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Decelle, Aurélien, Furtlehner, Cyril, Gómez, Alfonso De Jesus Navas, Seoane, Beatriz
Format:	Preprint
Published:	2023
Subjects:	Disordered Systems and Neural Networks Statistical Mechanics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fast training and sampling of Restricted Boltzmann Machines
by: Béreux, Nicolas, et al.
Published: (2024)

Inferring Higher-Order Couplings with Neural Networks
by: Decelle, Aurélien, et al.
Published: (2025)

A theoretical framework for overfitting in energy-based modeling
by: Catania, Giovanni, et al.
Published: (2025)

Uncovering statistical structure in large-scale neural activity with Restricted Boltzmann Machines
by: Béreux, Nicolas, et al.
Published: (2026)

The Copycat Perceptron: Smashing Barriers Through Collective Learning
by: Catania, Giovanni, et al.
Published: (2023)

Explaining the effects of non-convergent sampling in the training of Energy-Based Models
by: Agoritsas, Elisabeth, et al.
Published: (2023)

On the role of non-linear latent features in bipartite generative neural networks
by: Bonnaire, Tony, et al.
Published: (2025)

Cascade of phase transitions in the training of Energy-based models
by: Bachtis, Dimitrios, et al.
Published: (2024)

Thermodynamics of bidirectional associative memories
by: Barra, Adriano, et al.
Published: (2022)

The Symmetric Perceptron: a Teacher-Student Scenario
by: Catania, Giovanni, et al.
Published: (2026)

Replica Theory of Spherical Boltzmann Machine Ensembles
by: Tulinski, Thomas, et al.
Published: (2026)

Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machines
by: di Sarra, Giovanni, et al.
Published: (2026)

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems
by: Biazzo, Indaco
Published: (2023)

Fundamental operating regimes, hyper-parameter fine-tuning and glassiness: towards an interpretable replica-theory for trained restricted Boltzmann machines
by: Fachechi, Alberto, et al.
Published: (2024)

Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
by: D'Amico, Francesco, et al.
Published: (2025)

Damage spreading and coupling in spin glasses and hard spheres
by: Hukushima, Koji, et al.
Published: (2024)

Phase Transitions in single species Ising Models with Non-Reciprocal couplings
by: Garcés, Adrià, et al.
Published: (2024)

A solvable model of noisy coupled oscillators with fully random interactions
by: Ikeda, Harukuni
Published: (2026)

Irreversible Boltzmann samplers in dense liquids: weak-coupling approximation and mode-coupling theory
by: Ghimenti, Federico, et al.
Published: (2024)

Machine learning the Ising transition: A comparison between discriminative and generative approaches
by: Zhang, Difei, et al.
Published: (2024)

Boomerang effect in classical stochastic models
by: Zamora, Santiago, et al.
Published: (2024)

Machine learning analysis of dimensional reduction conjecture for nonequilibrium Berezinskii-Kosterlitz-Thouless transition in three dimensions
by: Haga, Taiki
Published: (2023)

Fermionic theory of nonequilibrium steady states
by: De Giuli, Eric, et al.
Published: (2023)

Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium
by: Carnazza, Francesco, et al.
Published: (2024)

Slow dissipation and spreading in disordered classical systems: A direct comparison between numerics and mathematical bounds
by: De Roeck, Wojciech, et al.
Published: (2023)

Predictive power of a Bayesian effective action for fully-connected one hidden layer neural networks in the proportional limit
by: Baglioni, P., et al.
Published: (2024)

Universal Scaling Laws of Absorbing Phase Transitions in Artificial Deep Neural Networks
by: Tamai, Keiichi, et al.
Published: (2023)

Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective
by: Ghio, Davide, et al.
Published: (2023)

Stochastic Gradient Descent-like relaxation is equivalent to Metropolis dynamics in discrete optimization and inference problems
by: Angelini, Maria Chiara, et al.
Published: (2023)

Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation
by: Gross, Markus, et al.
Published: (2023)

Analytic theory of dropout regularization
by: Mori, Francesco, et al.
Published: (2025)

Distinct mechanisms underlying in-context learning in transformers
by: Gibson, Cole, et al.
Published: (2026)

Interpreting the Synchronization Gap: The Hidden Mechanism Inside Diffusion Transformers
by: Albrychiewicz, Emil, et al.
Published: (2026)

Optimal Protocols for Continual Learning via Statistical Physics and Control Theory
by: Mori, Francesco, et al.
Published: (2024)

Two failure modes of deep transformers and how to avoid them: a unified theory of signal propagation at initialisation
by: Giorlandino, Alessio, et al.
Published: (2025)

Discrete generative diffusion models without stochastic differential equations: a tensor network approach
by: Causer, Luke, et al.
Published: (2024)

Factual recall in linear associative memories: sharp asymptotics and mechanistic insights
by: Giorlandino, Alessio, et al.
Published: (2026)

The Physics of Data and Tasks: Theories of Locality and Compositionality in Deep Learning
by: Favero, Alessandro
Published: (2025)

Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks
by: Bocchi, Dario, et al.
Published: (2026)

From Classical to Quantum: Extending Prometheus for Unsupervised Discovery of Phase Transitions in Three Dimensions and Quantum Systems
by: Yee, Brandon, et al.
Published: (2026)