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Eric Jang: How to Understand ML Papers Quickly
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My ML mentees often ask me some variant of the question "how do you choose which papers to read from the deluge of publications flooding Ar...
Eric Jang: How to Understand ML Papers Quickly Monday, January 25, 2021 How to Understand ML Papers Quickly My ML mentees often ask me some variant of the question "how do you choose which papers to read from the deluge of publications flooding Arxiv every day?” The nice thing about reading most ML papers is that you can cut through the jargon by asking just five simple questions. I try to answer these questions as quickly as I can when skimming papers. 1) What are the inputs to the function approximator? E.g. a 224x224x3 RGB image with a single object roughly centered in the view. 2) Wh
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