Comment by maf12

I think the main motivation in ml theory that touches current SOTA is not "expressing simple ideas with a jargon for show". Jargon is necessary, as much as some (mostly very practical) engineers or software people cannot see it due to how unnecessary it seems to them (as they are used to practically and quickly express themselves). It's a jargon for the mathematics of machine learning, which is pretty unstandardized so to speak. So you need to define yourself. And without a jargon and clear proofs, what you do is just brainstorming at most. The value of such work is that their statement is pretty clear, proved and contain hypotheses which can be tested by the future papers.

Here is an example: to explain the existence of adversarial example, there are 2 suggestions without a jargon: 1) that the decision boundary is too nonlinear, 2) that the decision boundary is too linear. Both of these explanations contradict and stated without any real proof and unfortunately can be widely heard in most of the adversarial example papers. If we were to have clear formulations of these two statements, we could have tested both of these claims but unfortunately the papers that suggested these theories didn't put effort for defining a jargon and putting their suggestion as a clear-formal statement.