How to Understand ML Papers Quickly | Eric Jang

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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 Arxiv every day?”

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) What are the outputs to the function approximator? E.g. a 1000-long vector corresponding to the class of the input i

How to Understand ML Papers Quickly | Eric Jang

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