> I might take up your challenge and write a blog post about it or something if I get any success. Success or failure, I would really enjoy seeing that write u

> I might take up your challenge and write a blog post about it or something if I get any success. Success or failure, I would really enjoy seeing that write up! I would be even more excited to be proven wrong. The promise of "universal function approximator" is very temping. My personal dream would be to have it so one could essentially run scipy in reverse and learn the entire library with a NN. Even in the case I gave, the idea that you could learn an arbitrary quantile function means you could also arbitrarily learn a sampler for any distribution, since all you have to do compose a uniform sampler with whatever quantile function you learned. Of course for this example I'm using "solved" using a similar approach with variational inference (pyro has a write up on it: https://pyro.ai/examples/svi_part_i.html https://pyro.ai/examples/svi_part_i.html, you might find David Blei's "Variational Inference: A Review for Statisticians" useful as well https://arxiv.org/abs/1601.00670 https://arxiv.org/abs/1601.00670)