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[D] What I’d like to write in my NeurIPS rebuttal

We thank the reviewers for the detailed comments, of which some were even based on our paper.

To the reviewer that said our paper was “underdeveloped” because we didn’t use a different methodology Y from field Z, we’d like to point out that a) this is in field A, b) we provided a framework for how to extend this to other methodologies in field A, and c) methodology Y has no obvious way to extend to the problem we’re addressing (and doing so would be a whole paper in its own right). Do you often read papers and get frustrated that they aren’t the papers you’ve written?

To the same reviewer, who asked why we didn’t cite papers Z1 and Z2, we would again point out that this isn’t field Z and those papers have no relevance to the topic at hand except that you’d have written a paper on a different topic, which we didn’t.

To the reviewer that asked why we didn’t cite X, we’d like to point out that we did cite X, and had a whole paragraph discussing the relationship of this work to that one.

To the reviewer that proposed an example dataset to evaluate our model on, we point out that we already evaluate the model on that data set; see our Experiments section.

To the reviewer that pointed out that our method won’t work when assumption 3 isn’t met, yes, you’re correct. That’s why we stated it as an assumption. Congratulations on your reading comprehension.

To the reviewer that directly copy/pasted our introduction into the “what 3 things does this paper contribute” box, we’ll be sure to include in future revisions a copy/paste-able review justifying “score 10, confidence 5” to make your review easier. That you also confused our main claim with a work we were citing, and otherwise completely missed the discussion on relationship to prior work or what makes this paper novel, makes your review particularly useful to development of the work.

To the reviewer that wrote that, while THEY were familiar with the definitions in a reference, we should explain it for readers that might be confused, we understand entirely. We’ll gladly explain it for “a friend of yours”, err “readers”, and not you, because you get it and you’re smart and it’s just the readers who don’t.

To the reviewer who commented that our results were “contradictory” because we said that our modification “in general performed slightly worse” on this metric, when in fact our plots show it sometimes performed better, we’ll gladly fix our claim to be clear that “in general” doesn’t mean “always” and also our results are even better than the previous wording indicated.

To the reviewer that said our comparison method’s results were worse than reported in the original paper, we’ve carefully compared their bar charts to ours and found that the results are the same to the precision of the graphical printout in the previous paper. If you could lend us your image sharpening function so we can get more significant digits out of their plot, we’d be glad to redo the comparison.

To the reviewer who used half of their review to argue that our entire subfield is dumb and wrong, we thank them for reaching across academic lines to provide commentary in an area that pains you deeply.

And finally, to the reviewers who called our paper (all actual quotes) “original, well-motivated, and worthy of study”, “important in its own right”, that said you “greatly enjoyed reading this paper” and that “this is an interesting problem and certainly worth studying” and that “this paper identifies an important problem … [and the authors] then present a simple” solution, thank you for also marking this a reject. Since all of you gave us scores between 5 and 3, neither the AC nor any of you will ever have to read this response or reconsider your scores before we are inevitably rejected, but we hope that your original, well-motivated, worth-studying, important, interesting, clear papers receive reviews of equal quality in the future!


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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.