Learning minimal representations of stochastic processes with variational autoencoders-Reference-Cited by-同舟云学术

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Learning minimal representations of stochastic processes with variational autoencoders

Published:2024-07-18 Issue:1 Volume:110 Page:
ISSN:2470-0045
Container-title:Physical Review E
language:en
Short-container-title:Phys. Rev. E

Author:

Fernández-Fernández Gabriel¹²^ORCID,Manzo Carlo³⁴^ORCID,Lewenstein Maciej¹²⁵^ORCID,Dauphin Alexandre¹²^ORCID,Muñoz-Gil Gorka⁶^ORCID

Affiliation:

1. ICFO–Institut de Ciències Fotòniques

2. Barcelona Institute of Science and Technology

3. Universitat de Vic–Universitat Central de Catalunya

4. Bioinformatics and Bioimaging, Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC), 08500 Vic, Barcelona, Spain

5. ICREA

6. University of Innsbruck

Funder

European Commission

Ministerio de Ciencia e Innovación

Generalitat de Catalunya

H2020 European Research Council

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevE.110.L012102/fulltext

Reference59 articles.

1. Machine learning and the physical sciences

2. Learning biophysical determinants of cell fate with deep neural networks

3. Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data

4. Unsupervised machine learning of topological phase transitions from experimental data

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1. Learning minimal representations of stochastic processes with variational autoencoders;Physical Review E;2024-07-18

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