Reviews: Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks

I think this paper is clearly written and makes some reasonable contributions, but could do with an editing pass to frame the approach a bit better and relate it to recent contributions that similarly seek to amortize inference in sequential models by training neural net proposals. While the authors are the first (to my knowledge) to train NN proposals for SMC in a procedural graphics / probabilistic programming setting, their approach is of course very closely related the one developed by Gu and colleagues [1], which the authors cite and the one proposed by Paige and Wood [2], which they do not. I am assuming this work was done more or less concurrently and independently, and I in principle don't see a problem for this paper from the point of view of novelty. That said, the paper in its current revision still reads a bit like a graphics paper that pitches using NN proposals for SMC as its core idea. This is unfortunate, in that it would have been nice to see the authors relate their work to that done by others.