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For my master thesis, I implemented a new and fancy algorithm. The code seemed to be fine and dandy for the usual, simple test cases. After using more elaborate
by meuk 9y ago
For my master thesis, I implemented a new and fancy algorithm. The code seemed to be fine and dandy for the usual, simple test cases. After using more elaborate test cases, I found cases that didn't work well.
After contacting the author, who indicated he didn't have such problems, I literally spent months trying to debug the code. When I finally gave up and re-wrote my implementation basically from scratch, and found the same problems, I contacted the author again. He then indicated that indeed there was a problem with the method for these cases, that he understood the problem and found a way to fix it. In hindsight, the problem was not hard to understand (but still, the claims in the paper were unwarranted IMO).
Conclusion? I wish I was a math prodigy, then I would have spotted the problem instantly. Also, be wary of claims made in papers.
- soVeryTired 9y agoMost papers are wrong. Not necessarily wrong in an essential way, but most of them overstate their claims. There's a rule of thumb in the machine learning community: if you want an algorithm that does X well, find a paper on doing X, and implement the method that the paper compares its proposal against. That way you're near state of the art, but you're also using a method that many people have used and tested.
- Q6T46nT668w6i3m 9y agoThis is new to me, but I think it’s fantastic advice!