  The standard interpretation of importance-weighted autoencoders is that they
maximize a tighter lower bound on the marginal likelihood than the standard
evidence lower bound. 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, present a tighter lower
bound, and visualize the implicit importance-weighted distribution.
