Not every deep learning paper is great. Is that a problem?

Deep learning is a field that’s growing quickly. That’s good! There are lots of new deep learning papers put out every day. That’s good too… right? What if not every paper out there is particularly good? What even makes a paper good in the first place? It’s an interesting thing to think about, and debate, since there’s no clean-cut answer and there are worthwhile arguments both ways. Wherever you find yourself coming down in the debate, though, you’ll appreciate the good papers that much more. Relevant links: https://blog.piekniewski.info/2018/07/14/autopsy-dl-paper/ https://www.reddit.com/r/MachineLearning/comments/90n40l/dautopsy_of_a_deep_learning_paper_quite_brutal/ https://www.reddit.com/r/MachineLearning/comments/agiatj/d_google_ai_refuses_to_share_dataset_fields_for_a/