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Mattias Ohlsson

Mattias Ohlsson

Professor

Mattias Ohlsson

Variational auto-encoders with Student’s t-prior

Author

  • Najmeh Abiri
  • Mattias Ohlsson

Summary, in English

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.

Department/s

  • Computational Biology and Biological Physics
  • eSSENCE: The e-Science Collaboration

Publishing year

2019

Language

English

Publication/Series

ESANN 2019 - Proceedings : The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Document type

Conference paper

Topic

  • Computer Science
  • Other Computer and Information Science

Conference name

27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference date

2019-04-24 - 2019-04-26

Conference place

Bruges, Belgium

Status

Published

Project

  • Lund University AI Research

ISBN/ISSN/Other

  • ISBN: 978-287-587-065-0