"if you haven't read them you also shouldn't cite them" -- this is wildly incorrect in an academic context. If I'm using ResNets, I should cite the original ResNet paper, even if I haven't read it. If I'm using Transformers, I should cite the original Transformer paper, even if I haven't read it. If my work is a direct extension of method B, and method B is a direct extension of method A, I should cite the source of A, even if I haven't read it.
You can't claim independence from past work simply because you didn't look directly at it. The job of an academic researcher is to know the landscape of relevant ideas, where they come from, where they're going, and to hopefully contribute a few new good ones.
Citation chains should extend back from your work, along a reasonable line conceptual inheritance, back to a reasonable point of origin. Schmidhuber has different definitions for both of these reasonables than the bulk of the ML research community, to a point that makes him difficult to satisfy.
It's worth pointing out that sometimes, some papers just become part of the general context of things and are no longer explicitly cited. Or people cite textbooks or general survey papers instead.
You make it sound like all original ideas from academia must be cited all the time, even if that was not the source of someone’s inspiration.
If I’m in the private sector, and I rediscover something from first principles, it is not my responsibility to go search all academia to see if someone’s done it before so I can cite their work.
If I rely on a code library that doesn’t explicitly cite papers it was built on, it is also not my responsibility to go find all the papers that it might’ve been built from and cite those papers.
> Of course, but if you haven't read them you also shouldn't cite them.
But if you build on them you should have read them. I don't know about the specifics and I don't know if Schmidhuber is out of line or not, and citations and impact factors are a terrible mess, but generally speaking, you are responsible for finding and reading and citing any related work that needs to be cited, and if you work on neural networks in an academic context you probably have been forced to read that particular one at some point. Citation obligations don't just disappear because you don't want to do the research.
You can't claim independence from past work simply because you didn't look directly at it. The job of an academic researcher is to know the landscape of relevant ideas, where they come from, where they're going, and to hopefully contribute a few new good ones.
Citation chains should extend back from your work, along a reasonable line conceptual inheritance, back to a reasonable point of origin. Schmidhuber has different definitions for both of these reasonables than the bulk of the ML research community, to a point that makes him difficult to satisfy.