"Preprint" implies prior to printing, which means that there's a reasonable expectation for this paper to be submitted, accepted, and printed in a scholarly journal.
What we have here is little more than a tongue-in-cheek submission which carries an aesthetic of "hot-take" throughout the paper. This is unbecoming of one committed to scholarly pursuits and all but guarantees rejection from journals committed to professionalism.
Furthermore, what's really interesting is how this comment section has developed. It really is the blind leading the blind here.
I will not subject myself further to the consequences of Brandolini's law except to implore the reader to consider the signal-to-noise ratio resulting from being too tolerant of posts like this.
This is still just name calling. You are just using negatively charged adjectives without quoting or arguing the substance or even the style. Is your crique only about the presentation or the substance of the ideas too?
What makes it unprofessional? To me it looks much better than a substantial chunk of my review stacks at ML conferences and journals. Are you an ML researcher? Maybe you're used to a different research community that's more "uptight"?
You're making a normative argument. The fact that other people publish crap is irrelevant, unless you actually intend to lend implicit justification of the status quo's existence just because it exists. "Ought", meet "is", etc.
The take proferred by TFA just isn't a useful take at all except perhaps for those who have never been elbow-deep in ML model architecture design, analysis, and training. The headline alludes to a surprising fact that you learn throughout course studies, a sidenote that can be repeatedly referred back to in order to emphasize the universality of statistical reasoning, but it's certainly not worthy of some kind of manifesto.
I agree we need to demystify ML for the common audience but this is a messaging problem much moreso than it is a pedagogical one. Typically the standard for publication is "genuine novel contribution" but no one who has been through a study regimen about ML will learn anything new from this. Preprints are supposed to be reserved for those papers which anticipate publication but I see no path for this paper to be accepted anywhere.
The paper offers a counterpoint to a published work (Zhang et al., 2021) which together with their earlier unpublished Arxiv version from 2016 has over 7000 citations. If you disagree with this rebuttal, by all means formulate what you find lacking.
> The fact that other people publish crap is irrelevant
You argued that this work is not something that can be seriously be considered to be submitted for publication and cannot be counted as a preprint. It has been pointed out that many works do get submitted to academic venues that aren't up to this quality. You're shifting goalposts.
You are making dismissive remarks without having to state your own view. Do you think Zhang et al's view is correct and deep learning shows novel effects that existing tools can't describe? Do you think the current manuscript does not effectively address those points? You have to argue if you think you have arguments. Labeling something a manifesto or a hot take is just low effort jab. Why do you think that the paper has no chance of acceptance? You are rehashing the same non-argument in different words.
A useful comment would state something like: the authors still do not explain effect X and Y that appears only in deep learning and not in classic ML. Or: the authors' point regarding effect X is incorrect and does not actually show what they claim to show. Etc. Simply saying "it's unserious" can be just turned back at your comment the same way.
> many works do get submitted to academic venues that aren't up to this quality
There's that normative argument rearing its head again. Not interested in jumping off bridges just because your friends do it, thanks.
It is unserious. The thesis amounts to "statistical models like these are mean-field, roughly-max-entropy approximations under the implied data generating process" which is not only an offhand comment a professor might make in ML 201 but tautological on its face. The fact they drag in a couple citations to say as much is besides the point entirely.
It's a higher quality article than half the submissions to NeuroIPS and the rest of the AI/ML conferences, because it has potential to remain relevant next year, and because of its high didactic content.
I wouldn't classify it as a "hot take".
