Critique

Overall, an excellent article - probably one of the best ways someone could have explained Normalizing Flows. Every equation is well-introduced beforehand and the gradual steps are easy to understand. Great job! :)

Critique
1) "As a result, the authors of this method train their flow using the reverse KL." It would be great if you can explain how reverse KL is different from forward KL (and how that helps with transformations that aren't invertible).
2) Although very well written, overall, the article feels a little short. Would it be possible to include some further details (i.e. pros and cons of normalizing flows, why choose normalizing flows over other generative models, some applications of normalizing flows etc.)?

Minor edits
1) Since the analytic form of $p(\mathbf {x} )$ is not generally known, *it *is *modelled by transforming samples from a source distribution $p(\mathbf {z} )$ through a transformation $T$, *which *is generally parameterized with some parameters $\theta$.

2) Such a transformation must be complex enough to model the data distribution. [This sentence has been accidentally split across two lines].
3) To *achieve this complexity in the finite normalizing flow paradigm, the overall transformation...
4) Then, we define the transformation of each partition *separately...
5) *Notably, unlike the finite normalizing flows, computing...
6) Training continuous normalizing flows is also challenging, as it requires *backpropagating through the ODE solver.