Asset Details

  • Description:
  • a Standard generative model. b Stochastic autoencoder with tied observations. c Equivalent tied stochastic autoencoder with AutoGen parameterisation
  • License:
  • Rights Managed
  • Rights Holder:
  • Springer Nature
  • License Rights Holder:
  • © The Author(s) 2019
  • Asset Type:
  • Image
  • Asset Subtype:
  • Figure
  • Image Orientation:
  • Landscape
  • Image Dimensions:
  • 948 x 495
  • Image File Size:
  • 30.6 KB
  • Creator:
  • Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber
  • Credit:
  • Mansbridge, A., Fierimonte, R., Feige, I., & Barber, D. (2019). Improving latent variable descriptiveness by modelling rather than ad-hoc factors. Machine Learning, 108(8-9), 1601-1611. https://doi.org/10.1007/s10994-019-05830-1.
  • Collection:
  • Keywords:
  • Generative modelling, Latent variable modelling, Variational autoencoders, Variational inference, Natural language processing
  • Restrictions:
  • Property Release:
  • No
  • Model Release:
  • No
  • Purchasable:
  • Yes
  • Sensitive Materials:
  • No
  • Article Authors:
  • Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber
  • Article Copyright Year:
  • 2019
  • Publication Volume:
  • 108
  • Publication Issue:
  • 8-9
  • Publication Date:
  • 07/22/2019
  • DOI:
  • https://doi.org/10.1007/s10994-019-05830-1

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