Submitted by RobbinDeBank t3_z2hr3p in MachineLearning
Insighteous t1_ixh7wbc wrote
So this is how research works? Like a big fat self-marketing campaign. Disgusting. Was it always like that? What is with (my idealized) imagination of working together in a big scientific community and enhance knowledge for everyone. Or what is this about?
ReginaldIII t1_ixhgfbo wrote
Imagine your peer reviewed publications were routinely uncited by your immediate peers and they often claimed novelty in ideas you had already published about.
Schmidhuber literally just wants to be cited when people refer to work that they did.
new_name_who_dis_ t1_ixhxgdg wrote
> Schmidhuber literally just wants to be cited when people refer to work that they did.
He has some of the most cited papers in the field. What Schmidhuber wants is to be cited for papers that almost no one read and whose ideas can be vaguely relevant to some of the new breakthrough papers, but only if you really squint.
He's a very good researcher and and has many cool ideas, and it'd be much better if he was actually encouraging people to adopt them the proper way (like by creating demos and easy to use libraries and opensourcing code/weights) -- instead of trying to prove that the already widely adopted techniques are actually special cases of his own techniques.
ReginaldIII t1_ixi0bfl wrote
Look up how often people like LeCun actively avoid citing his "most cited papers in the field" out of little more than unprofessional spite.
> it'd be much better if he was actually encouraging people to adopt them the proper way
He is and does. That's literally why they are highly cited papers in the first place.
His argument for not being cited isn't against the wider community who do cite him. It's against the major players who actively refuse to cite him.
mcbainVSmendoza t1_ixjdvwa wrote
"but only if you really squint" Bingo. That's what feels so petty to me. That's where you really see ego behind the wheel.
crouching_dragon_420 t1_ixi9vba wrote
Have you ever read some random RNN paper from LeCun's group and noticed they didn't cite the LSTM paper but instead cited the GRU paper, which is a watered-down version of the LSTM?
new_name_who_dis_ t1_ixie59m wrote
GRU cites LSTM paper so it's fine imo, especially if they're using the GRU architecture and not the LSTM architecture.
Citing the original LSTM paper is kind of dumb in general since the modern LSTM architecture is not the one described in the paper. You really need to cite one of the latter papers that introduced the Forget gate, if you are using the default LSTM implementation.
crouching_dragon_420 t1_ixilxbs wrote
That's total horseshit when the architecture in the paper is almost the same as the original LSTM. I'm not talking about modern papers. If they cite GRU, they should cite LSTM as well. I dont agree with the saying GRU cite LSTM so it's fine to cite GRU but not LSTM. That's shouldnt be how credit assignment work.
DigThatData t1_ixinfbc wrote
> If they cite GRU, they should cite LSTM as well.
that's not how citations work...
> GRU cite LSTM so it's fine to cite GRU but not LSTM.
but that's literally how citations work. If you cite paper X, you are implicitly citing everything that paper X cited as well. citation graphs are transitive.
new_name_who_dis_ t1_ixiofup wrote
Yea exactly. If you’re citing a paper you’re implicitly citing all of the papers that paper cited.
No one is citing the original perceptron paper even though pretty much every deep learning paper uses some form of a perceptron. Because the citation is implied going from more complex architectures cited, to simpler ones those cited, and so on until you get to perceptron.
alwayslttp t1_ixj9zpy wrote
All metrics are stacked massively in favour of first level citations - many entirely ignore second level and beyond. For example, a paper's "cited by" count is its most prominent metric of influence/importance, and is a count of how many papers directly cite it.
I don't know this particular beef, but it sounds like citing GRU and not LSTM is a potential sleight/insult here. Exactly the kind of thing you see in petty academic rivalries. You're explicitly deciding who you're crediting with the key innovations you're building from, and you know that most people aren't chasing every sub reference of every citation.
DigThatData t1_ixkghj5 wrote
sounds like the problem here is the metrics then. which also is something I'm pretty sure only even became a thing extremely recently. For a long time, the only citation-based metric anyone talked about was their Erdos number, which was a tongue-in-cheek thing anyway. Concern over metrics like this is more likely than not going to damage research progress by encouraging gamification. The only "cited by" count I ever concern myself with is for sorting stuff on google scholar, which I never presume is an exact count or directly maps to the sorting I really need.
MTGTraner t1_ixhasus wrote
>Was it always like that?
Always has been 🌏👨🚀🔫👨🚀
I recently learned about Rosalind Franklin's sad story behind the scenes of the discovery of the structure of DNA through Walter Isaacson's CRISPR book. I am sure that there are even older examples of gross behavior in the history of science.
-gh0stRush- t1_ixi4td8 wrote
Einstein's wife was a mathematician who helped him develop his theories. He privately wrote about her assistance but never publicly credited her for any of his results.
mca_tigu t1_ixhb11u wrote
It always was, see e.g. https://en.m.wikipedia.org/wiki/Leibniz–Newton_calculus_controversy
machinelearner77 t1_ixhcdck wrote
> Like a big fat self-marketing campaign. Disgusting.
You mean the Canada-US researcher circle jerk, don't you?
MartianTomato t1_ixhle92 wrote
Yes. In my conversations with people thinking about what topics / research to work on, I'd say number of citations / marketability is top 3 factor in their decision making. I also see people draw an equivalence between # of citations and impact.
The flaw in this reasoning is that most highly cited work (in "hot" topics) is, by its nature, replaceable. If you don't do it, someone else inevitably will. And this kind of research feels empty in the same way that software engineering does... the researcher has become a replaceable cog in the machine learning machine. Yet somehow, I see people are more motivated to pursue topics they feel they will be "scooped" on if they delay even one conference cycle...
DevFRus t1_ixihbol wrote
I feel you.
> I see people are more motivated to pursue topics they feel they will be "scooped" on if they delay even one conference cycle...
I also see this often and so I use the inverse of this as the guiding principle in much of my work. If I feel like this is a topic I'd get 'scooped' on if I didn't publish quickly enough then I look for another topic to work on. It usually feels nicer to do 'slow' research. However, it can be a bit isolating.
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