I just put online a preprint of the “Importance Weighted Hierarchical Variational Inference” paper.

The paper proposes a novel and efficient multisample variational upper bound on log q(z|x) in case of hierarchical proposal q(z|x). This way one can use Neural Samplers (like VAE) as expressive proposal distributions, allowing us to learn more expressive models p(x). This is enabled by a novel multisample variational upper bound on the marginal log-density, which generalizes and bridges several prior results.

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