Comment by erostrate | Hacker News Reader

Comment by erostrate | Hacker News Reader

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> credit belongs to the people who deserve credit for the work they've done that was useful to others

Certainly agree. The point is that coming up with the idea, writing it as an equation, or an architecture diagram in a paper, is a small fraction of the effort that goes into making the idea work in a model showing good performance on real life datasets.

For example, just taking a random paper that Schmidhuber claims should give him credit for GANs, https://people.idsia.ch/~juergen/FKI-126-90ocr.pdf hopefully you can easily see that a lot of work would be needed to turn this into a realistic image generation model. And that is, even if you admit that the idea is strongly related to GANs, which I'm not convinced of but won't spend time on.

> Credit belongs to whoever actually makes it work. >> That is according to whom? Is it a rule you just came up with or accepted practice? And if it's accepted practice, in what community is it accepted practice?

It is accepted practice in the ML community. If it weren't, Schmidhuber wouldn't be complaining.

I agree a significant amount of work (and often insight too) is needed to translate an architecture idea into something that works in practice, and there are certainly plenty of ideas that are obvious in the abstract. But I also think it's important to avoid dismissing work only on the basis that it doesn't involve "real life datasets".

Deep learning is a relatively unexplored field and there are many open mathematical and scientific questions to ask that involve only model equations or contrived datasets. Novel theoretical results are not just about some architecture idea but about proving facts that can be useful for understanding how the model class would perform in different scenarios. Which in turn can help shape the search space for applied work.

Additionally, I don't think credit assignment should be so discrete. 100% agree that vomiting out vague ideas shouldn't grant claims to credit, but academic science much too often gives only a single author the "real" credit.

Incidentally, in other fields the person who actually makes it work very well may not be the person that receives this credit. Like biology can involve a lot of hard manual work (that isn't really intellectual) in order to realize a project plan. It varies how much of the credit those people receive, and I'm not even sure how much they should receive. This topic is extremely nuanced.

In many fields, this is how citations would work

"Introductory theoretical work in GAN was done by Schmidhuber [1], but it was not until large experimental efforts [2,3,4] on image generations that the power of GANs was revealed."

Yep, the article is presumably for the reader, so context should be provided.

I don't buy that this is standard practice in the ML community, and even if it is it's BS. If the basic idea/principle has been published previously but in a different context you should cite it and say why the solution is not directly applicable or has not been evaluated in the current context. Anything else is unprofessional.

Agreed. The piece anticipated this straw man argument:

> "the inventor of an important method should get credit for inventing it. She may not always be the one who popularizes it. Then the popularizer should get credit for popularizing it (but not for inventing it)." Nothing more or less than the standard elementary principles of scientific credit assignment.[T22] LBH, however, apparently aren't satisfied with credit for popularising the inventions of others; they also want the inventor's credit.[LEC]

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