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"Mathiness" as the author puts it is one of my main complaints of many academic papers, this is not at all limited to ML. I think we really need to get away from typical math notation and start to present the calculations as code. This is more readable to the modern practicing professionally and the algorithm's degree of parallelization opportunities (or troubling lack thereof) would be more clear.

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The paper needs to explain what is being done, why and under what assumptions. As long as each of those are clearly explained, in my experience, it doesn't really matter whether each of them is explained in words/math/code. Yes, one form might be a little bit more work for people from a certain background, but that's a hump you can get over relatively easily after reading a few papers on the topic. The worst is when you have to guess at what the authors might be doing and why they're doing it that way, and what assumptions they might be operating under... and all they've done is dumped their results within the page limit and before the conference submission deadline.

I've struggled with that a couple of times.

Anecdotal evidence: I wrote a technical report describing an algorithm and its novel applications, etc. A good compromise between a high-level description of the concepts and a documentation of the actual code (and thus a guide for implementing it), in my opinion.

I tried to turn that into a scientific paper and it was rejected. Changed the entire notation to a "math-y" notation that actually "obfuscates or impresses rather than clarifies" (direct quote from the article). Accepted with positive comments on the very same aspects that got it rejected in the first place. This in a Computer Science conference.

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