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[D] Are there any papers with Normalizing Flow-based generative models that show empirical results on 1d/2d densities?

All the normalizing flows-based papers I read (NICE, RealNVP, Glow, etc.) show experiments on high dimensional image datasets. I am looking for works that analyze the capacity of NFs to learn simple 1/2d distributions. I am aware of the 2d experiments in [Rezende and Mohamed, 2015] but, as far as I understand, for the 2d datasets they train by directly minimizing KL (and do not train using samples) because the analytic inverse of Planar flow does not exist.

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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.