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The abstract is damning, but not surprising to anyone outside the machine learning bubble. It's a symptom of a major problem in meta-science: breakthroughs are
by xamuel 7y ago
The abstract is damning, but not surprising to anyone outside the machine learning bubble. It's a symptom of a major problem in meta-science: breakthroughs are hard, and when you shovel billions of dollars into trying to incentivize breakthroughs, you end up with a lot of envelope-pushing dressed up like breakthroughs, and most of them don't even successfully push the envelope.
- dekhn 7y agobiology, especially genomics, is this way as well. Every day you see research articles where a scientist studied something and the PR folks (or even the paper) present it as a solution to a major medical problem. I spent a good part of my graduate career attempting to write code that duplicated the results in papers; I'd say about 75% of the time, I convinced myself the paper was simply wrong due to technical errors that a bit of data science was able to uncover.
- hyperbovine 7y ago> not surprising to anyone outside the machine learning bubble. Or in it, either. ML researchers are painfully aware of how the sausage gets made. (About 12 hours before the NIPS deadline, typically.)
- fock 7y ago12 hours, that's early, according to a friend of mine doing his PhD in ML/NN (or as he put it: everyone uses L²-loss and SGD - it's just not original)... He also thinks that the quality of papers from a formal standpoint is very low (not speaking of content yet). Riddled with spelling errors and illogical sentences. But as published around NIPS gives all the credits and fixing those things does not (moreso, it probably taints results), this won't change anytime soon...
- deleted 7y ago[deleted]