Paper authors (and this posts author apparently) like to throw in lots of scary-looking maths to signal that they are smart and that what they are doing has merit. The Reinforcement Learning field is particularly notorious for doing this, but it's all over ML. Often it is not on purpose, everyone is taught this is the proper "formal" way to express these things, and that any other representation is not precise or appropriate in a scientific context.
In practice, when it comes down to code, even without higher-level libraries, it is surprisingly simple, concise and intuitive.
Most of the math elements used have quite straightforward properties and utility, but of course if you combine them all together into big expressions with lots of single-character variables, it's really hard to understand for everyone. You kind of need to learn to squint your eyes and understand the basic building-blocks that the maths represent, but that shouldn't be necessary if it wasn't obfuscated like this.
I’m going to push back on this a bit. I think a simpler explanation (or at least one that doesn’t involve projecting one’s own insecurities onto the authors) is that the people who write these papers are generally comfortable enough with mathematics that they don’t believe anything has been obfuscated. ML is a mathematical science and many people in ML were trained as physicists or mathematicians (I’m one of them). People write things this way because it makes symbolic manipulations easier and you can keep the full expression in your head; what you’re proposing would actually make it significantly harder to verify results in papers.
But my experience as a mathematician tells me another part of that story.
Certain fields are much more used to consuming (and producing) visual noise in their notation!
Some fields have even superfluous parts in their definitions and keep them around out of tradition.
It's just as with code: Not everyone values writing readable code highly. Some are fine with 200 line function bodies.
And refactoring mathematics is even harder: There's no single codebase and the old papers don't disappear.