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[R] Importance Weighted Hierarchical Variational Inference

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.

Paper | Talk | Blogpost

submitted by /u/asobolev
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.