A challenge when reading an ML paper is that the authors don’t have the time or space to explain and clarify every advanced concept, theorem, or notation they used to get the to result they present in the paper. At best, they will point to a text book or a review paper if the concept is novel enough from the point of their target audience, other times they will just assume that their readers are smart/educated enough to figure out the necessary concepts on their own. But most readers (like myself) aren’t that smart.

When I was in grad school, I had the luxury and time to go through the references one by one, look up theorems and text books etc, and dedicate several weeks to building up the necessary theoretical baggage to grasp a paper that I was really interested in.

But now I work in industry, and I don’t have the time to pick a text book on graph theory or algebraic topology, if a concept from those fields is used to illustrate or prove a point in a theoretical ML paper, or read an additional 10 papers besides the one I am actually interested in. In fact I barely have time to read papers in general.

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Do you have any hacks/heuristics to quickly get up to speed on the necessary theoretical backgrounds for an advanced ML paper, without having to dedicated several weeks to going through graduate level textbooks, and “reverse reading” bibliographies until you get to a paper that simplifies a given concept?