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Variational auto-encoders with Student’s t-prior
Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-1145-4297
2019 (English)In: ESANN 2019 Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges – 24-26 April 2019, Bruges: ESANN , 2019, p. 415-420Conference paper, Published paper (Refereed)
Abstract [en]

We propose a new structure for the variational auto-encoders (VAEs) prior, with the weakly informative multivariate Student’s t-distribution. In the proposed model all distribution parameters are trained, thereby allowing for a more robust approximation of the underlying data distribution. We used Fashion-MNIST data in two experiments to compare the proposed VAEs with the standard Gaussian priors. Both experiments showed a better reconstruction of the images with VAEs using Student’s t-prior distribution. © 2019 ESANN (i6doc.com). All rights reserved.

Place, publisher, year, edition, pages
Bruges: ESANN , 2019. p. 415-420
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Other Computer and Information Science
Identifiers
URN: urn:nbn:se:hh:diva-41247Scopus ID: 2-s2.0-85071324436ISBN: 978-287-587-065-0 (electronic)OAI: oai:DiVA.org:hh-41247DiVA, id: diva2:1378365
Conference
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2019), Bruges, Belgium, April 24-26, 2019
Available from: 2019-12-13 Created: 2019-12-13 Last updated: 2019-12-19Bibliographically approved

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Ohlsson, Mattias

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Citation style
  • apa
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  • de-DE
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  • nn-NB
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More languages
Output format
  • html
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  • asciidoc
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