# 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)