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Free Dynamics of Feature Learning Processes

Journal of statistical physics, 2023-03, Vol.190 (3), Article 51

Furtlehner, Cyril

2023

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Creating artificial human genomes using generative neural networks

PLoS genetics, 2021-02, Vol.17 (2), p.e1009303-e1009303

2021

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Restricted Boltzmann machine: Recent advances and mean-field theory

Chinese physics B, 2021-04, Vol.30 (4), p.40202-25

2021

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Deep convolutional and conditional neural networks for large-scale genomic data generation

PLoS computational biology, 2023-10, Vol.19 (10), p.e1011584-e1011584

2023

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Learning a restricted Boltzmann machine using biased Monte Carlo sampling

SciPost physics, 2023-03, Vol.14 (3), p.032, Article 032

2023

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Inferring effective couplings with restricted Boltzmann machines

SciPost physics, 2024-04, Vol.16 (4), p.095, Article 095

2024

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Exact Training of Restricted Boltzmann Machines on Intrinsically Low Dimensional Data

Physical review letters, 2021-10, Vol.127 (15), p.1-158303, Article 158303

2021

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Stochastic Dynamics of Discrete Curves and Multi-Type Exclusion Processes

Journal of statistical physics, 2007-06, Vol.127 (5), p.1049-1094

2007

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Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics

Journal of statistical physics, 2018-09, Vol.172 (6), p.1576-1608

2018

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Gaussian-spherical restricted Boltzmann machines

Journal of physics. A, Mathematical and theoretical, 2020-05, Vol.53 (18), p.184002

2020

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Approximate inverse Ising models close to a Bethe reference point

Journal of statistical mechanics, 2013-09, Vol.2013 (9), p.P09020-35

Furtlehner, Cyril

2013

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Spectral dynamics of learning in restricted Boltzmann machines

Europhysics letters, 2017-09, Vol.119 (6), p.60001

2017

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Data Stream Clustering With Affinity Propagation

IEEE transactions on knowledge and data engineering, 2014-07, Vol.26 (7), p.1644-1656

2014

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IEEE_Xplore

Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines

Journal of statistical mechanics, 2022-11, Vol.2022 (11), p.114009

2022

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Short-Term Forecasting of Urban Traffic Using Spatio-Temporal Markov Field

IEEE transactions on intelligent transportation systems, 2022-08, Vol.23 (8), p.10858-10867

2022

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IEEE_Xplore

Deep convolutional and conditional neural networks for large-scale genomic data generation

PLoS computational biology, 2023-10, Vol.19 (10)

2023

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Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines This article is an updated version of: Decelle A, Furtlehner C and Seoane B 2021 Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines Advances in Neural Information Processing Systems vol 34 ed M Ranzato, A Beygelzimer, Y Dauphin, P S Liang and J Wortman Vaughan (New York: Curran Associates) pp 5345–59

Journal of statistical mechanics, 2022-11, Vol.2022 (11)

2022

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Cycle-Based Cluster Variational Method for Direct and Inverse Inference

Journal of statistical physics, 2016-08, Vol.164 (3), p.531-574

2016

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Learning a restricted Boltzmann machine using biased Monte Carlo sampling

SciPost physics, 2023-03, Vol.14 (3)

2023

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Pairwise MRF Models Selection for Traffic Inference

Interdisciplinary Information Sciences, 2013, Vol.19(1), pp.17-22

FURTLEHNER, Cyril

2013

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Latent binary MRF for online reconstruction of large scale systems

Annals of mathematics and artificial intelligence, 2016-06, Vol.77 (1-2), p.123-154

2016

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Traffic data reconstruction based on Markov random field modeling

Inverse problems, 2014-02, Vol.30 (2), p.25003-14

2014

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One-Dimensional Particle Processes with Acceleration/Braking Asymmetry

Journal of statistical physics, 2012-07, Vol.147 (6), p.1113-1144

2012

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Learning multiple belief propagation fixed points for real time inference

Physica A, 2010, Vol.389 (1), p.149-163

2010

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Dynamical windings of random walks and exclusion models. Part I: Thermodynamic limit in Z2

Journal of statistical physics, 2004, Vol.114 (1-2), p.229-260

2004

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Free Dynamics of Feature Learning Processes

Creating artificial human genomes using generative neural networks

Restricted Boltzmann machine: Recent advances and mean-field theory

Deep convolutional and conditional neural networks for large-scale genomic data generation

Learning a restricted Boltzmann machine using biased Monte Carlo sampling

Inferring effective couplings with restricted Boltzmann machines

Exact Training of Restricted Boltzmann Machines on Intrinsically Low Dimensional Data

Stochastic Dynamics of Discrete Curves and Multi-Type Exclusion Processes

Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics

Gaussian-spherical restricted Boltzmann machines

Approximate inverse Ising models close to a Bethe reference point

Spectral dynamics of learning in restricted Boltzmann machines

Data Stream Clustering With Affinity Propagation

Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines

Short-Term Forecasting of Urban Traffic Using Spatio-Temporal Markov Field

Deep convolutional and conditional neural networks for large-scale genomic data generation

Cycle-Based Cluster Variational Method for Direct and Inverse Inference

Learning a restricted Boltzmann machine using biased Monte Carlo sampling

Pairwise MRF Models Selection for Traffic Inference

Latent binary MRF for online reconstruction of large scale systems

Traffic data reconstruction based on Markov random field modeling

One-Dimensional Particle Processes with Acceleration/Braking Asymmetry

Learning multiple belief propagation fixed points for real time inference

Dynamical windings of random walks and exclusion models. Part I: Thermodynamic limit in Z2

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