Autopsy of a deep learning paper

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". Machine learning sits somewhere in between. There are examples of clear scientific papers (such as e.g. the paper that introduced the backprop itself) and there are examples of clearly engineering papers where a solution to a very particular practical problem is described.