Reinterpreting Importance-Weighted Autoencoders

Chris Cremer, Quaid Morris, David Duvenaud

Feb 17, 2017 (modified: Feb 27, 2017) ICLR 2017 workshop submission readers: everyone
  • Abstract: The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood. We give an alternate interpretation of this procedure: that it optimizes the standard variational lower bound, but using a more complex distribution. We formally derive this result, and visualize the implicit importance-weighted approximate posterior.
  • TL;DR: IWAE optimizes the standard variational lowerbound, but using a more complex variational distribution
  • Keywords: Unsupervised Learning
  • Conflicts: cs.toronto.edu, harvard.edu, cam.ac.uk

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