of the approach and experiments or list future direction for readers. The writeup is exceptionally clear and well organized-- full marks! I have only minor feedback to improve clarity: 1. Add a few more sentences explaining the experimental setting for continual learning 2. In Fig 3, explain the correspondence between the learning curves and M-PHATE. Why do you want to want me to look at the learning curves? Does worse performing model always result in structural collapse? What is the accuracy number? For the last task? or average? 3. Make the captions more descriptive. It's annoying to have to search through the text for your interpretation of the figures, which is usually on a different page 4. Explain the scramble network better... 5. Fig 1, Are these the same plots, just colored differently? It would be nice to keep all three on the same scale (the left one seems condensed) M-PHATE results in significantly more interpretable visualization of evolution than previous work. It also preserves neighbors better (Question: why do you think t-SNE works better in two conditions? The difference is very small tho). On continual learning tasks, M-PHATE clearly distinguishes poor performing learning algorithms via a collapse. (See the question about this in 5. Improvement). The generalization vignette shows that the heterogeneity in M-PHATE output correlates with performance. I would really like to recommend a strong accept for this paper, but my major concern is that the vignettes focus on one dataset MNIST and one NN architecture MLP, which makes the experiments feel incomplete. The results and observations made by authors would be much more convincing if they could repeat these experiments for more datasets and NN architectures.
1. Add a few more sentences explaining the experimental setting for continual learning 2. In Fig 3, explain the correspondence between the learning curves and M-PHATE. Why do you want to want me to look at the learning curves? Does worse performing model always result in structural collapse? What is the accuracy number? For the last task? or average?