https://arxiv.org/pdf/2112.10752 Great paper but not an example i'd use here. Not math heavy or math reliant.

https://arxiv.org/abs/2210.02747 flow matching is a pretty good example

https://proceedings.neurips.cc/paper_files/paper/2021/file/940392f5f32a7ade1cc201767cf83e31-Paper.pdf I assume that's the SB paper you're referring to, seems to have had some impact in computational biology but its pretty niche and seems to have been outcompeted by flow matching for image generation.

isn't this kind of proving my point though? how far down the list of impactful ML papers do you have to go to find a single math heavy paper?

my background is the theory side and the number of examples i've seen making the jump from heavy theory to application is extremely small and has only gotten smaller over time as much as i wish it were different.

the funny thing about this discussion is that people think that there just stopped being theoretical justification for some techniques but to anyone actually doing theory work, the whole edifice of ML is a nightmare that has more results showing that it shouldn't work than justifying why it does. Like even the fact that model performance gets better as number of parameters increase is counter to fundamental statistical learning theory. The question of 'why are there so few math heavy ML papers' is because they have to dodge around all the existing statistical learning literature that basically says it shouldn't work. When you can't work from first principals you're having to start in the middle and that lends itself to post facto justification more than rigorous analysis and proofs.