Critique

Overall Thoughts

• Outstanding work. I only have minor suggestions but overall I thought this was very clear and well done

Abstract

• Nit: I’m not sure if the ML or DL links in the Builds On section are all that helpful. Obviously attention is a ML/DL method but are you really reading this page if you don’t know about machine learning? I would suggest dropping the machine learning link at least.

Introduction

• I find it confusing why you refrained from using the full citation in the introduction, but plan to use it throughout the rest of the wiki page. I would pick one convention and use it throught instead of adding the Note at the end of the intro

Preliminaries

• I would move the blurb about v, W_1 and W_2 being parameters of the model to directly after they are introduce and before the Scaled dot product attention.

Paper 1

• Seeing as the Kendall rank correlation is the basis of the analysis of RQ1, it would be nice to have a description of it, or a citation I could follow to understand how this method works.
• I think the Datasets and Tasks section needs a trim. Although very thorough, reading through the rest of the experiments, I didn’t think that an understanding of each dataset was necessary to understand the author’s conclusions about attention weights.
• When discussing Figure 2 you reference the MIMIC dataset, but I can’t see MIMIC labeled on that figure.

=Paper 2

• No comments. Great section. I thought each research question was covered well and the figures and tables were introduced clearly

Hello Matthew!

Thank you so much for your excellent and comprehensive critique. Based on your suggestions, here are the changes that I made:

1) Removed the ML links from the "Builds On" section.
2) Edited the introduction to include the full citation.
3) Moved the text about v, W_1 and W_2 being parameters.
4) Provided a description (+ citation) for Kendall rank correlation in the first paragraph of Paper 1 -> Answering RQ1.
5) Trimmed down the "Datasets and Tasks" section by only including one sentence definitions for each dataset.
6) Edited the MIMIC example (it is now changed to Diabetes).

Once again, thank you for reviewing my page. Do let me know if you have any additional comments or suggestions.