Here’s why and how
Each day, more than 100 new computer science and machine learning papers are listed on arXiv. Though the works are not necessarily peer-reviewed before listing, this still is an enormous wealth of information. To get an impression, see the below chart for the growth of monthly submissions since 2009, taken from arXiv:
Doing the math, let’s assume that one needs 3 hours to read a paper from end to end, on average. At the numbers listed above, one would need 300 hours (or 12 days!) to read through them all. And that is just going through the papers of one day — the next day, we’d have to start anew; going through a similar number of publications again. Obviously, that’s not feasible, neither for experts nor for beginners.
Generally, as a beginner in machine learning, you are likely asking: do I need to read papers? And, given that there are so many, how can I do it at all? Here’s why and how!
A paper is a lecture: to be accepted at top-tier ML conferences, publications need to be crisp in their writing. They include an introduction to the topic, a method section, results, and a summary. Altogether, the content of a paper is a (condensed) lecturing on a single, very narrow topic. For…