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The seems to be a tacit belief in the ML community that you should include at least a few equations for your paper to be considered rigorous. Math notation as a cargo-cult token of rigor. But math, like figures, like English, is just a language to express and develop ideas.
You can think rigorously without mathematical notation. Inversely, some math-heavy papers use math to obfuscate a lack of rigor -- leveraging the fact that it's harder to read for most, and that it confers an appearance of scientificness.
Using math is like the choice of a programming language: for some classes of problems, it can make you a lot more productive. You can go a very long way without math (maybe all the way, if you have code instead). But if you master math, that's a key advantage you should leverage
Being highly mathematical is surprisingly rare in the ML community. At this point, it's almost like a superpower. Use math to think, not to impress
@mathbabedotorg calls the latter "mathematical snake oil".
If there isn't yet a study on whether including math formulæ in papers, even gratuitous ones, increases chances of publication, there should be one.https://twitter.com/TyphonBaalAmmon/status/808228153932054528 …
I prefer math to be expressed in a programming language style, where meaningful variable names are encouraged.
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