Critique

Diffusion models are a very hot topic rn, and the fact that it builds on Markov Chains and VI makes it an organic fit for our course material. Very good topic!

For paper 1:

• Don't forget to cite papers :)
• Visualizations of the steps are clearer than math.
• some visualizations for the first paper experiments, such as on cifar 10 would make the model convincing.

For Denoising Diffusion Probabilistic Models, you mentioned [2] "is extremely flexible, but this is somewhat to its detriment." I would love to see some elaborations on this. (The visualization/experiment section could be a great foreshadowing of this issue).

Additionally, the jump from Paper [2] to [3] needs some more justifications, its not obvious to the reader without the proper background, something along the line of "the parameters needed to train a DM is unfeasibly large, ... in this paper, the author considers simplifying it."

This and the problem above is actually just one problem, and there need to be more words dedicated to addressing the disadvantages of paper 1 and why paper 2 is a good choice to remedy 1.

• I like the derivations, but the theoretical guarantees seem to be missing
• similar to the theoretical guarantees, the experiments are also not addressed so it is unconvincing to readers that these models are as powerful and popular as they are.

Although it's not mandatory, a conclusion section really ties the page together, as right now the page seems to abruptly end, with no discussion or any justifications on why these models are significant.

Thanks for your comments Yilin. I've done my best to address your comments

• I added some extra citations for related works and datasets on which the papers were evaluated
• I included the figure from the original paper on the swiss roll dataset and moved that up to the top as I think it does a good job illustrating the distributions for the forward and reverse trajectories
• I added the experimental results for the first paper, including CIFAR-10. The quality of these results is what I use to motivate the advances made in the second paper
• I'm not exactly sure what you mean by theoretical guarantees? As far as I'm aware there aren't any guarantees on the model output which I can described
• I added some experimental results for both papers.
• I added a conclusion.