Feedback

(I am late because this page did not exist when I gave my other feedback).

You need to give explicit reference to the source of all figure that are not yours on the main page. Finding the source of the figures through clicking on them is not enough.

It would be good to untangle "auto-encoder" from "variational auto-encoder"; much of what you describe is just an auto-encoder (e.g., Figure 1). You should first say what a auto-encoder is, and then what is special about a variational auto-encoder. Perhaps also say what a probabilistic auto-encoder is (the model that the variational auto-encoder is a variational approximation to).

Can you please provide an intuitive high-level overview before you do the math (e.g., in the SGVB estimator section; the previous section was good, but this was impenetrable (for me at least)).

I found it difficult to work out what paper 2 was doing. What was its contribution? What problem does it solve? Is the difference that z is decomposed into multiple latent variables? (Are these assumed to be independent?) When you say "direct[ed] graphical model" which one are you talking about? (Was the x in the first paper also a set of random variables?) I'm not sure how the semi-supervised part works.

I think that you need more of a tutorial introduction. Someone who reads it should be enticed to read the papers.

There are still a few typos (e.g, escope)