Phase transition in the detection of modules in sparse networks - Aurélien Decelle; Florent Krzakala; Cristopher Moore; Lenka Zdeborová (CoRR, 2011)

Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications - Aurélien Decelle; Florent Krzakala; Cristopher Moore; Lenka Zdeborová (CoRR, 2011)

Computational Complexity, Phase Transitions, and Message-Passing for Community Detection. - Aurélien Decelle; Janina Hüttel; Alaa Saade; Cristopher Moore (CoRR, 2014)

Detection of cheating by decimation algorithm. - Shogo Yamanaka; Masayuki Ohzeki; Aurélien Decelle (CoRR, 2014)

Spectral Learning of Restricted Boltzmann Machines. - Aurélien Decelle; Giancarlo Fissore; Cyril Furtlehner (CoRR, 2017)

Thermodynamics of Restricted Boltzmann Machines and related learning dynamics. - Aurélien Decelle; Giancarlo Fissore; Cyril Furtlehner (CoRR, 2018)

Learning a Gauge Symmetry with Neural Networks. - Aurélien Decelle; Victor Martin-Mayor; Beatriz Seoane (CoRR, 2019)

Gaussian-Spherical Restricted Boltzmann Machines. - Aurélien Decelle; Cyril Furtlehner (CoRR, 2019)

Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines. - Giancarlo Fissore; Yufei Han; Aurélien Decelle; Cyril Furtlehner (STAIRS@ECAI, 2020)

Cascade of Phase Transitions for Multi-Scale Clustering. - Tony Bonnaire; Aurélien Decelle; Nabila Aghanim (CoRR, 2020)

Encoding large scale cosmological structure with Generative Adversarial Networks. - Marion Ullmo; Aurélien Decelle; Nabila Aghanim (CoRR, 2020)

Restricted Boltzmann Machine, recent advances and mean-field theory. - Aurélien Decelle; Cyril Furtlehner (CoRR, 2020)

Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines. - Aurélien Decelle; Cyril Furtlehner; Beatriz Seoane (NeurIPS, 2021)

The mighty force: statistical inference and high-dimensional statistics. - Erik Aurell; Jean Barbier; Aurélien Decelle; Roberto Mulet (CoRR, 2022)

Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling. - Nicolas Béreux; Aurélien Decelle; Cyril Furtlehner; Beatriz Seoane (CoRR, 2022)

Thermodynamics of bidirectional associative memories. - Adriano Barra; Giovanni Catania; Aurélien Decelle; Beatriz Seoane (CoRR, 2022)

Regularization of Mixture Models for Robust Principal Graph Learning. - Tony Bonnaire; Aurélien Decelle; Nabila Aghanim (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Explaining the effects of non-convergent MCMC in the training of Energy-Based Models. - Elisabeth Agoritsas; Giovanni Catania; Aurélien Decelle; Beatriz Seoane (ICML, 2023)

Explaining the effects of non-convergent sampling in the training of Energy-Based Models. - Elisabeth Agoritsas; Giovanni Catania; Aurélien Decelle; Beatriz Seoane (CoRR, 2023)

Unsupervised hierarchical clustering using the learning dynamics of RBMs. - Aurélien Decelle; Lorenzo Rosset; Beatriz Seoane (CoRR, 2023)

The Copycat Perceptron: Smashing Barriers Through Collective Learning. - Giovanni Catania; Aurélien Decelle; Beatriz Seoane (CoRR, 2023)

Inferring effective couplings with Restricted Boltzmann Machines. - Aurélien Decelle; Cyril Furtlehner; Alfonso De Jesus Navas Gómez; Beatriz Seoane (CoRR, 2023)

Deep convolutional and conditional neural networks for large-scale genomic data generation. - Burak Yelmen; Aurélien Decelle; Leila Lea Boulos; Antoine Szatkownik; Cyril Furtlehner; Guillaume Charpiat; Flora Jay (PLoS Comput. Biol., 2023)

Cascade of phase transitions in the training of energy-based models. - Dimitrios Bachtis; Giulio Biroli; Aurélien Decelle; Beatriz Seoane (NeurIPS, 2024)

Predicting large scale cosmological structure evolution with GAN-based autoencoders. - Marion Ullmo; Nabila Aghanim; Aurélien Decelle; Miguel Aragon-Calvo (CoRR, 2024)

Fast, accurate training and sampling of Restricted Boltzmann Machines. - Nicolas Béreux; Aurélien Decelle; Cyril Furtlehner; Lorenzo Rosset; Beatriz Seoane (CoRR, 2024)

Fast training and sampling of Restricted Boltzmann Machines. - Nicolas Béreux; Aurélien Decelle; Cyril Furtlehner; Lorenzo Rosset; Beatriz Seoane (ICLR, 2025)

Inferring High-Order Couplings with Neural Networks. - Aurélien Decelle; Alfonso De Jesus Navas Gómez; Beatriz Seoane (CoRR, 2025)

A theoretical framework for overfitting in energy-based modeling. - Giovanni Catania; Aurélien Decelle; Cyril Furtlehner; Beatriz Seoane (CoRR, 2025)

Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics. - Alessandra Carbone; Aurélien Decelle; Lorenzo Rosset; Beatriz Seoane (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

On the role of non-linear latent features in bipartite generative neural networks. - Tony Bonnaire; Giovanni Catania; Aurélien Decelle; Beatriz Seoane (CoRR, 2025)