Autopsy of a deep learning paper

Introduction

I read a lot of deep learning papers, typically a few/week. I've read probably several thousands of papers. My general problem with papers in machine learning or deep learning is that often they sit in some strange no man's land between science and engineering, I call it "academic engineering". Let me describe what I mean:

1. A scientific paper IMHO, should convey an idea that has the ability to explain something. For example a paper that proves a mathematical theorem, a paper that presents a model of some physical phenomenon. Alternatively a scientific paper could be experimental, where the result of an experiment tells us something fundamental about the reality. Nevertheless the central point of a scientific paper is a relatively concisely expressible idea of some nontrivial universality (and predictive power) or some nontrivial observation about the nature of reality.
2. An engineering paper shows a method of solving a particular problem. Problems may vary and depend on an application, sometimes they could be really uninteresting and specific but nevertheless useful for somebody somewhere. For an engineering paper, things that matter are different than for a scientific paper: the universality of solution may not be of paramount importance. What matters is

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