Inferring effective couplings with restricted Boltzmann machines

Autor: Aurélien Decelle, Cyril Furtlehner, Alfonso De Jesús Navas Gómez, Beatriz Seoane

Publikováno v: SciPost Physics, Vol 16, Iss 4, p 095 (2024)

Generative models offer a direct way of modeling complex data. Energy-based models attempt to encode the statistical correlations observed in the data at the level of the Boltzmann weight associated with an energy function in the form of a neural net

Externí odkaz: https://doaj.org/article/abc391adb5714d18be2c12a505d8015d

Creating artificial human genomes using generative neural networks.

Autor: Burak Yelmen, Aurélien Decelle, Linda Ongaro, Davide Marnetto, Corentin Tallec, Francesco Montinaro, Cyril Furtlehner, Luca Pagani, Flora Jay

Publikováno v: PLoS Genetics, Vol 17, Iss 2, p e1009303 (2021)

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create re

Externí odkaz: https://doaj.org/article/26fab8b8ed544e6ba6fddb07b388dbde

Regularization of Mixture Models for Robust Principal Graph Learning

Autor: Aurélien Decelle, Nabila Aghanim, Tony Bonnaire

Publikováno v: IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2021, pp.1-1. ⟨10.1109/TPAMI.2021.3124973⟩
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp.1-1. ⟨10.1109/TPAMI.2021.3124973⟩

A regularized version of Mixture Models is proposed to learn a principal graph from a distribution of $D$-dimensional data points. In the particular case of manifold learning for ridge detection, we assume that the underlying manifold can be modeled

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::346800e23caa0f2b0958fd45786863e3
https://hal.archives-ouvertes.fr/hal-03477742

An Introduction to Machine Learning: a perspective from Statistical Physics

Autor: Aurélien Decelle

Publikováno v: Physica A: Statistical Mechanics and its Applications. :128154

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::23dca15c5d28796ab5cd7e0a755cbe54
https://doi.org/10.1016/j.physa.2022.128154

Creating artificial human genomes using generative neural networks

Autor: Aurélien Decelle, Davide Marnetto, Luca Pagani, Linda Ongaro, Burak Yelmen, Francesco Montinaro, Cyril Furtlehner, Corentin Tallec, Flora Jay

Publikováno v: PLoS Genetics
PLoS Genetics, Public Library of Science, 2021, ⟨10.1371/journal.pgen.1009303⟩
PLoS Genetics, Vol 17, Iss 2, p e1009303 (2021)
PLoS Genetics, 2021, ⟨10.1371/journal.pgen.1009303⟩

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create re

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ca588c55f6037c093a0655064e02bcf
http://hdl.handle.net/11577/3376932