Yes, but passing through the editorial board takes time. In some fields research moves extremely fast and by the time papers is properly published it's already obsolete.
Machine learning is a field that's moving quickly and can also largely be checked quickly by other people in the field too. The problems that can't be checked easily are also those that won't be checked by the journals anyway (no journal I know of will retrain one of googles deep nets to see if they get the same result before publishing).
And please, I see what's happening here: I have been portrayed as the "traditional journal apologist" although it's definitely what I wanted here. I simply wanted to express my doubts that pre-printing is the answer to it all.
Anyway, is there a study that shows how many pre-printed articles in ML have been retracted or refuted so far? Until this is done, and shown as small as in peer-reviewed journals, keep a small basket.
I understand the process fully. It doesn't make it fast, nor does it make it a necessary cost to pay. Delaying access to content for several years does not solve a problem. A not-insignificant time was spent bouncing between people to sort out who was paying for the costs, then there is also a delay between acceptance and publication. This now averages just a month in pubmed, but papers can bounce around this point for a lot longer.
I am not arguing for pre-prints to replace traditional publishing, but the speed of spreading information is undeniably faster, and that's what started this whole chain of comments.
> Anyway, is there a study that shows how many pre-printed articles in ML have been retracted or refuted so far? Until this is done, and shown as small as in peer-reviewed journals, keep a small basket.
I don't get the phrase "keep a small basket", but no I've not seen this. The point is simply that PEERS (if we need to shout the word) can in many cases replicate the work and assess the results much more quickly than the traditional review & edit process. I picked the field because I see people re-implement work described in the papers very quickly, or the original authors share the trained models and code.
I'd also caution against using "retracted" papers as a measure, some journals charge for a retraction.
ArXiv is working very well for a lot of scientists in Machine Learning, since must results can be doublechecked by simply running the code. I won't disregard a proved approach that I've myself seen working just because it hasn't been published yet.
Could you provide some examples for a layman? To me it always seems that science moves far slower than one could hope for (e.g.: Every 'breakthrough' in batteries/Graphene of the last ten years and still no products)