I've only recently started reading about deep neural networks, and the thing that strikes me the most about the literature is the lack of mathematics . Open a NIPS paper from 2010 or so, and you'll see extremely dense mathematics: nonparametrics, variational approximation, sampling theory, riemannian geometry. But from my (admittedly small) sampling of the convnet / RNN literature there really doesn't seem to be much…
This sounds like science to me, is "we tried this, and it worked" something to shy away from for some reason? Of course, it's super important to publish "we tried this, and it didn't work." But that's another topic altogether...
If less math really is a trend, do you see it as a bad thing? You didn't state it strongly, but you've hinted at that, and it seems like the replies assume that's what you're suggesting.
I love math and mathy papers, and I would still welcome a trend toward less math in papers in return for more effort spent on making simple ideas plain and easily understood. But academics don't always operate that way. Math in papers is often used to obscure simple ideas, sometimes on purpose, and sometimes it's an indicator that the author doesn't understand the domain clearly enough but still wants to sound smart. Sometimes a paper really requires dense math, but not very often. Dense math almost always makes a paper more difficult to reproduce. Either way it is harder for even experts to evaluate the quality of dense math than of expository writing that strives for simplicity and clarity.
Neural networks are really simple math under the hood, well understood algorithms and simple linear algebra, why not write great papers that work and don't re-hash the math but instead focus on clarity, reproducibility and results?