Cascade of phase transitions in the training of energy-based models - Bachtis, Dimitrios; Biroli, Giulio; Decelle, Aurelien; Seoane, Beatriz (Journal Of Statistical Mechanics-Theory And Experiment, 2025)

Explaining the effects of non-convergent sampling in the training of energy-based models* - Agoritsas, Elisabeth; Catania, Giovanni; Decelle, Aurelien; Seoane, Beatriz (Journal Of Statistical Mechanics-Theory And Experiment, 2025)

Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics - Carbone, Alessandra; Decelle, Aurelien; Rosset, Lorenzo; Seoane, Beatriz (Ieee Transactions On Pattern Analysis And Machine Intelligence, 2025)

Inferring High-Order Couplings with Neural Networks - A Decelle; AJN Gómez; B Seoane (Arxiv Preprint Arxiv, 2025)

A theoretical framework for overfitting in energy-based modeling - G Catania; A Decelle; C Furtlehner; B Seoane (Arxiv Preprint Arxiv, 2025)

On the role of non-linear latent features in bipartite generative neural networks - T Bonnaire; G Catania; A Decelle; B Seoane (Arxiv Preprint Arxiv, 2025)

Copycat perceptron: Smashing barriers through collective learning - Catania, Giovanni; Decelle, Aurelien; Seoane, Beatriz (Physical Review e, 2024)

Inferring effective couplings with restricted Boltzmann machines - Decelle, Aurelien; Furtlehner, Cyril; Gomez, Alfonso De Jesus Navas; Seoane, Beatriz (Scipost Physics, 2024)

Predicting large scale cosmological structure evolution with GAN-based autoencoders - M Ullmo; N Aghnim; A Decelle; M Aragon-Calvo (Arxiv Preprint Arxiv, 2024)

Fast, accurate training and sampling of Restricted Boltzmann Machines - N Béreux; A Decelle; C Furtlehner; L Rosset; B Seoane (Arxiv Preprint Arxiv, 2024)

An Introduction to Machine Learning: a perspective from Statistical Physics - Decelle, Aurelien (Physica A-Statistical Mechanics And Its Applications, 2023)

Deep convolutional and conditional neural networks for large-scale genomic data generation - Yelmen, Burak; Decelle, Aurelien; Boulos, Leila Lea; Szatkownik, Antoine; Furtlehner, Cyril; Charpiat, Guillaume; Jay, Flora (Plos Computational Biology, 2023)

Unsupervised hierarchical clustering using the learning dynamics of restricted Boltzmann machines - Decelle, Aurelien; Seoane, Beatriz; Rosset, Lorenzo (Physical Review e, 2023)

Cosmology with cosmic web environments II. Redshift-space auto and cross-power spectra - Bonnaire, Tony; Kuruvilla, Joseph; Aghanim, Nabila; Decelle, Aurelien (Astronomy & Astrophysics, 2023)

Thermodynamics of bidirectional associative memories - Barra, Adriano; Catania, Giovanni; Decelle, Aurelien; Seoane, Beatriz (Journal Of Physics A-Mathematical And Theoretical, 2023)

Learning a restricted Boltzmann machine using biased Monte Carlo sampling - Bereux, Nicolas; Decelle, Aurelien; Furtlehner, Cyril; Seoane, Beatriz (Scipost Physics, 2023)

Fast and Functional structured data generator - A Carbone; A Decelle; L Rosset; B Seoane (Icml, 2023)

The mighty force: statistical inference and high-dimensional statistics - E Aurell; J Barbier; A Decelle; R Mulet (Spin Glass Theory And Far Beyond: Replica Symmetry Breaking After, 2023)

Regularization of Mixture Models for Robust Principal Graph Learning - T Bonnaire; A Decelle; N Aghanim (Ieee Transactions On Pattern Analysis And Machine Intelligence, 2022)

Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines* - Decelle, Aurelien; Furtlehner, Cyril; Seoane, Beatriz (Journal Of Statistical Mechanics-Theory And Experiment, 2022)

Disordered Physics for Biology and Artificial Intelligence - DECELLE, AURELIEN FABRICE (Autor o Coautor) (2022)

Cosmology with cosmic web environments I. Real-space power spectra - Bonnaire, Tony; Aghanim, Nabila; Kuruvilla, Joseph; Decelle, Aurelien (Astronomy & Astrophysics, 2022)

Biased Monte Carlo sampling in RBMs - B Seoane; A Decelle; C Furtlehner; N Bereux (Aps March Meeting Abstracts, 2022)

Física Estadística de modelos basados en una energía - DECELLE, AURELIEN FABRICE (Investigador principal (IP)) (2022)

Inverse problems for structured datasets using parallel TAP equations and restricted Boltzmann machines - Decelle, Aurelien; Hwang, Sungmin; Rocchi, Jacopo; Tantari, Daniele (Scientific Reports, 2021)

Exact Training of Restricted Boltzmann Machines on Intrinsically Low Dimensional Data - Decelle, A; Furtlehner, C (Physical Review Letters, 2021)

Encoding large-scale cosmological structure with generative adversarial networks - Ullmo, Marion; Decelle, Aurelien; Aghanim, Nabila (Astronomy & Astrophysics, 2021)

Restricted Boltzmann machine: Recent advances and mean-field theory* - A Decelle; C Furtlehner (Chinese Physics b, 2021)

Creating artificial human genomes using generative neural networks - Yelmen, Burak; Decelle, Aurelien; Ongaro, Linda; Marnetto, Davide; Tallec, Corentin; Montinaro, Francesco; Furtlehner, Cyril; Pagani, Luca; Jay, Flora (Plos Genetics, 2021)

Cascade of phase transitions for multiscale clustering - Bonnaire, Tony; Decelle, Aurelien; Aghanim, Nabila (Physical Review e, 2021)

Gaussian-spherical restricted Boltzmann machines - Decelle, Aurelien; Furtlehner, Cyril (Journal Of Physics A-Mathematical And Theoretical, 2020)

T-ReX: a graph-based filament detection method - Bonnaire, Tony; Aghanim, Nabila; Decelle, Aurelien; Douspis, Marian (Astronomy & Astrophysics, 2020)

La macchina di Boltzmann: quando il modello di Ising incontra il Machine Learning - A Decelle (Ithaca: Viaggio Nella Scienza, 2020)

The Boltzmann Machine: when the Ising model meet Machine Learning - A Decelle (Ithaca: Viaggio Nella Scienza, 2020)

Learning a local symmetry with neural networks - Decelle, A; Martin-Mayor, V; Seoane, B (Physical Review e, 2019)

Robust multi-output learning with highly incomplete data via restricted Boltzmann machines - G Fissore; A Decelle; C Furtlehner; Y Han (Arxiv Preprint Arxiv, 2019)

Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics - Decelle, A; Fissore, G; Furtlehner, C (Journal Of Statistical Physics, 2018)

Spectral dynamics of learning in restricted Boltzmann machines - Decelle, A; Fissore, G; Furtlehner, C (Epl, 2017)

Data quality for the inverse Ising problem - A Decelle; F Ricci-Tersenghi; P Zhang (Journal Of Physics A-Mathematical And Theoretical, 2016)

Cycle-Based Cluster Variational Method for Direct and Inverse Inference - Furtlehner, Cyril; Decelle, Aurelien (Journal Of Statistical Physics, 2016)

Solving the inverse Ising problem by mean-field methods in a clustered phase space with many states - Decelle, Aurelien; Ricci-Tersenghi, Federico (Physical Review e, 2016)

Inference of the sparse kinetic Ising model using the decimation method - Decelle, Aurelien; Zhang, Pan (Physical Review e, 2015)

Detection of Cheating by Decimation Algorithm - Yamanaka, Shogo; Ohzeki, Masayuki; Decelle, Aurelien (Journal Of The Physical Society Of Japan, 2015)

Belief-propagation-guided Monte-Carlo sampling - Decelle, Aurelien; Krzakala, Florent (Physical Review b, 2014)

Ensemble renormalization group for the random-field hierarchical model - Decelle, Aurelien; Parisi, Giorgio; Rocchi, Jacopo (Physical Review e, 2014)

Pseudolikelihood Decimation Algorithm Improving the Inference of the Interaction Network in a General Class of Ising Models - Decelle, Aurelien; Ricci-Tersenghi, Federico (Physical Review Letters, 2014)

Computational Complexity, Phase Transitions, and Message-Passing for Community Detection - A Decelle; J Hüttel; A Saade; C Moore (Arxiv Preprint Arxiv, 2014)

Extreme Value Statistics Distributions in Spin Glasses - Castellana, Michele; Decelle, Aurelien; Zarinelli, Elia (Physical Review Letters, 2011)

Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications - A Decelle; F Krzakala; C Moore; L Zdeborová (Physical Review e, 2011)

Inference and Phase Transitions in the Detection of Modules in Sparse Networks - Decelle, Aurelien; Krzakala, Florent; Moore, Cristopher; Zdeborova, Lenka (Physical Review Letters, 2011)

Statistical physics of disordered networks-Spin Glasses on hierarchical lattices and community inference on random graphs - A Decelle (Université Paris Sud-Paris Xi, 2011)

Finite-size fluctuations of the critical temperature in mean-field and short-range spin) glasses: a numerical study - M Castellana; A Decelle; E Zarinelli (Arxiv Preprint Arxiv, 2011)

Hierarchical Random Energy Model of a Spin Glass - Castellana, Michele; Decelle, Aurelien; Franz, Silvio; Mezard, Marc; Parisi, Giorgio (Physical Review Letters, 2010)

Message passing for optimization and control of a power grid: Model of a distribution system with redundancy - L Zdeborová; A Decelle; M Chertkov (Physical Review e, 2009)

Archimedean lattices in the bound states of wave interacting particles - Eddi, A; Decelle, A; Fort, E; Couder, Y (Epl, 2009)

Cascade of phase transitions in the training of Energy-based models - D Bachtis; G Biroli; A Decelle; B Seoane (Arxiv Preprint Arxiv, 2024)

Explaining the effects of non-convergent MCMC in the training of Energy-Based Models - E Agoritsas; G Catania; A Decelle; B Seoane (International Conference On Machine Learning, 2023)

Explaining the effects of non-convergent MCMC in the training of Energy-Based Models - Agoritsas, Elisabeth; Catania, Giovanni; Decelle, Aurelien; Seoane, Beatriz (Critical Care Medicine, 2023)

Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines - Decelle, Aurelien; Furtlehner, Cyril; Seoane, Beatriz (Advances In Neural Information Processing Systems, 2021)

Creating artificial human genomes using generative neural networks - Yelmen, B; Decelle, A; Ongaro, L; Marnetto, D; Tallec, C; Montinaro, F; Furtlehner, C; Pagani, L; Jay, F (European Journal Of Human Genetics, 2020)

Learning a Gauge Symmetry with Neural Networks - A Decelle; V Martin-Mayor; B Seoane (Arxiv Preprint Arxiv, 2019)

Spectral dynamics of learning in restricted Boltzmann machines - A Decelle; G Fissore; C Furtlehner (Europhysics Letters, 2017)

Archimedean lattices in the bound states of wave interacting particles - A Eddi; A Decelle; E Fort; Y Couder (Europhysics Letters, 2009)