Adversarial variational bayes toy example.ipynb

What does making p(x) "close" to p(z) even mean? Those are distributions over different spaces, how can one compare them?

That's a typo, should be p(x) close to q(x)

Thanks for the great notebook! I have two questions though:

• I don't understand why you make the the output of the generative network a stochastic tensor. In the paper in figure 1 the eps just gets added/multiplied. Shouldn't it be sufficient as an output?
• Shouldn't the weights of q and p be updated separately? The paper states two different loss terms for them in the algorithm. Or is that possible because of proposition 2?

• The StochasticTensor in the generative model is used to keep track of the sample x ~ p(x|z) and the density p(x|z) so we can evaluate it when computing the log probability of the data given the sampled latent state, z.

• In practice we don't have acess to T* and use the current discriminator T as a replacement. T does not directly depend on the parameters of p, so d/dp -T(x, z) is 0, and the gradients are identical to the gradients using the separate losses in the paper.

Makes sense! Thank you for taking the time to reply!

Thank you for your interesting post. Please forgive me if I ask a dumb question. How could I find "stochastic_tensor" module on tensorflow? I use the version 1.8 installed with pip but it says there is no such module. Thank you.