Articles
Restricted Boltzmann Machine and statistical physics
- Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics, A. Decelle, G. Fissore, C. Furltlehner, Journal of Stat. Physics 172, 6, pp1576-1608
- Spectral dynamics of learning in restricted Boltzmann machines, A. Decelle, G. Fissore, C. Furtlehner, Euro. Phys. Lett. 119, 60001, p6 (2017)
- Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines, G. Fissore, A. Decelle, C. Furtlehner, Y. Han; STAIRS (2020)
- Gaussian-spherical restricted Boltzmann machines, A. Decelle, C. Furtlehner; J. Phys. A: Math. Theor. 53, 184002 (2020)
Application to Bioinformatics
- Creating Artificial Human Genomes Using Generative Models, B. Yelmen et al., biorXiv:769091 (2019)
Cosmology: application of inference and machine learning
- T-ReX: a graph-based filament detection method, T. Bonnaire, A. Aghanim, A. Decelle, M. Doupsis; A&A 637, A18 (2020)
- Cascade of Phase Transitions for Multi-Scale Clustering, T. Bonnaire, A. Decelle, A. Aghanim; arXiv:2010.07955 (2020)
Spin Glasses, sampling, optimization and Machine Learning
- Hierarchical Random Energie model of a spin glass, M. Castellana, A. Decelle, S. Franz, M. mézard, G. Parisi, Phys. Rev. Lett., 104, 127206 (2010)
- Extreme Value Statistics Distributions in Spin Glasses, M. Castellana, A. Decelle, E. Zarinelli, Phys. Rev. Lett., 107, 275701 (2011)
- Belief-Propagation Guided Monte-Carlo Sampling, A. Decelle, F. Krzakala, Phys. Rev. B, 89, 214421 (2014)
- Ensemble renormalization group for the random field hierarchical model, A. Decelle, G. Parisi, J. Rocchi, Phys. Rev. E, 89, 032132 (2014)
- Cycle-Based Cluster Variational Method for Direct and Inverse Inference, C. Furtlehner, A. Decelle; J. Stat. Phys., 164, 164:531–574 (2016)
- Learning a Local Symmetry with Neural-Networks, A. Decelle, V. Martin-Mayor, B. Seoane; Phys. Rev. E, 100, 050102 (2019)
Ising inverse problem
- Decimation based algorithm to improve inference using the Pseudo-Likelihood, A. Decelle, F. Ricci-Tersenghi, Phys. Rev. Lett., 112, 070603 (2013)
- Detection of cheating by decimation algorithm, S. Yamanka, M. Ohzeki, A. Decelle, J. Phys. Soc. Jpn. 84, 024801 (2015)
- Solving the inverse Ising problem by mean-field methods in a clustered phase space with many states, A. Decelle, F. Ricci-Tersenghi, Phys. Rev. E, 94, 012112 (2016)
- Inference of the sparse kinetic Ising model using the decimation method, A. Decelle, P. Zhang, Phys. Rev. E, 91, 052136 (2015)
- Data quality for the inverse lsing problem, A. Decelle, F. Ricci-Tersenghi, P. Zhang; J. Phys. A (2016)
- Inverse problems for structured datasets using parallel TAP equations and RBM , A. Decelle, S. Hwang, J. Rocchi, D. Tantari; arXiv:1906.11988 (2019)
Inference in complex network
- Inference and Phase Transitions in the Detection of Modules in Sparse Networks, A. Decelle, F. Krzakala, C. Moore, L .Zdeborova, Phys. Rev. Lett., 107, 065701 (2011)
- Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications, A. Decelle, F. Krzakala, C. Moore, L .Zdeborova, Phys. Rev. E 84, 066106 (2012)
Smart grid and Message-passing
- Message passing for optimization and control of a power grid: Model of a distribution system with redundancy, L. Zdeborova, A. Decelle, M. Chertkov, Phys. Rev. E 80, 046112 (2009)
Bouncing droplets
- Archimedean lattices in the bound states of wave interacting particles, A. Eddi, A. Decelle, E. Fort, Y. Couder, Euro. Phys. Lett. 87, 56002 (2009)