Event generation and statistical sampling for physics with deep generative models and a density information buffer

Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.

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Autores principales:	Sydney Otten, Sascha Caron, Wieske de Swart, Melissa van Beekveld, Luc Hendriks, Caspar van Leeuwen, Damian Podareanu, Roberto Ruiz de Austri, Rob Verheyen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/dee282af39824482b8165ce8053b6102
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id	oai:doaj.org-article:dee282af39824482b8165ce8053b6102
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spelling	oai:doaj.org-article:dee282af39824482b8165ce8053b61022021-12-02T16:51:34ZEvent generation and statistical sampling for physics with deep generative models and a density information buffer10.1038/s41467-021-22616-z2041-1723https://doaj.org/article/dee282af39824482b8165ce8053b61022021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22616-zhttps://doaj.org/toc/2041-1723Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.Sydney OttenSascha CaronWieske de SwartMelissa van BeekveldLuc HendriksCaspar van LeeuwenDamian PodareanuRoberto Ruiz de AustriRob VerheyenNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Sydney Otten Sascha Caron Wieske de Swart Melissa van Beekveld Luc Hendriks Caspar van Leeuwen Damian Podareanu Roberto Ruiz de Austri Rob Verheyen Event generation and statistical sampling for physics with deep generative models and a density information buffer
description	Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.
format	article
author	Sydney Otten Sascha Caron Wieske de Swart Melissa van Beekveld Luc Hendriks Caspar van Leeuwen Damian Podareanu Roberto Ruiz de Austri Rob Verheyen
author_facet	Sydney Otten Sascha Caron Wieske de Swart Melissa van Beekveld Luc Hendriks Caspar van Leeuwen Damian Podareanu Roberto Ruiz de Austri Rob Verheyen
author_sort	Sydney Otten
title	Event generation and statistical sampling for physics with deep generative models and a density information buffer
title_short	Event generation and statistical sampling for physics with deep generative models and a density information buffer
title_full	Event generation and statistical sampling for physics with deep generative models and a density information buffer
title_fullStr	Event generation and statistical sampling for physics with deep generative models and a density information buffer
title_full_unstemmed	Event generation and statistical sampling for physics with deep generative models and a density information buffer
title_sort	event generation and statistical sampling for physics with deep generative models and a density information buffer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dee282af39824482b8165ce8053b6102
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Event generation and statistical sampling for physics with deep generative models and a density information buffer

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