Critique

I think the article is a really good length and explains the important details of how the lower bound changes based on the architecture. I can also clearly see the contribution of one paper over the other!

Abstract

• A sentence could be added to mention that a comparison between the performance of the two architectures is done at the end.

Stochastic Recurrent Networks

• I am wondering what g represents.

Figures

• The figures could be made bigger.
• It may also be helpful to move the graphical representation of STORN and VRNN to be in between the two sections so that the reader comes across it earlier.
• It may be helpful to mention that it is a condensed representation and there may be multiple features, hidden states, and k latent variables.

Minor Corrections

Variational Recurrent Neural Networks

• In general, this function is a highly flexible, such as a neural network. -> In general, this function is highly flexible, such as a neural network.