The Most Cited AI Papers in 2022
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The Most Cited AI Papers in 2022
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Re: The Most Cited AI Papers in 2022
#2It looks like it might be more interesting on what industries or problem domains those most cited AI papers were focused.
Re: The Most Cited AI Papers in 2022
#3Did nothing of note happen outside of deep learning the whole year?
Re: The Most Cited AI Papers in 2022
#4I'm getting wildly different citation counts for some of the listed AI papers.
For example, the paper "ColabFold: making protein folding accessible to all" is listed as having 1162 citations. I'm seeing that it was cited only by 899 publications on Scite: https://scite.ai/reports/colabfold-making-protein-folding-ac...
I'm wondering if Google Scholar is overestimating or Scite is underestimating.
Re: The Most Cited AI Papers in 2022
#5It looks like it might be more interesting on what industries or problem domains those most cited AI papers were focused.
According to Fig 3, Google was that most-cited problem domain. The most cited papers are for Google problems. You must have Google money and compute to achieve top-cited research.
Re: The Most Cited AI Papers in 2022
#6very useful but flawed.
the list biases against papers published later in the year.
this is a problem raised by openai and brain researchers: https://twitter.com/_jasonwei/status/1631935794301771777
Re: The Most Cited AI Papers in 2022
#7I'm getting wildly different citation counts for some of the listed AI papers. For example, the paper "ColabFold: making protein folding accessible to all" is listed as having 1162 citations. I'm seeing that it was cited only by 899 publications on Scite: https://scite.ai/reports/colabfold-making-protein-folding-ac... I'm wondering if Google Scholar is overestimating or Scite is underestimating.
Citation counts are always a bit arbitrary. Google Scholar usually overestimates, because it's basically a bunch of heuristics. Curated citation databases underestimate in the name of consistency. For example, they may ignore citations in conference proceedings, as conference papers are not considered legitimate publications in most fields.
Re: The Most Cited AI Papers in 2022
#8I'm getting wildly different citation counts for some of the listed AI papers. For example, the paper "ColabFold: making protein folding accessible to all" is listed as having 1162 citations. I'm seeing that it was cited only by 899 publications on Scite: https://scite.ai/reports/colabfold-making-protein-folding-ac... I'm wondering if Google Scholar is overestimating or Scite is underestimating.
Semantic Scholar has 1,111. [0]
I tend to trust Semantic more than GS. GS tends to overestimate. For example on GS I have 164 citations on one paper and semantic says 150. FWIW Scite says 49.[1]
[0] https://www.semanticscholar.org/paper/ColabFold%3A-making-pr...
[1] I'll note that this paper is an arxiv paper and has not been accepted at a conference but I'd also argue that conference acceptance means little in ML. I'll explain if anyone is actually concerned with the claim.
Re: The Most Cited AI Papers in 2022
#9Did nothing of note happen outside of deep learning the whole year?
Of course interesting things happen outside of mainstream deep learning. The problem is that sorting by citation count is a terrible way to look broadly at research, because mainstream deep learning is so hyped up that there are just way more people working in that area than in other areas, and with more people come more citations.
It's a shame because I think that other areas offer much more interesting and technical questions, but many new researchers are only exposed to mainstream deep learning because the enormous hype drowns out everything else.
Re: The Most Cited AI Papers in 2022
#10Incredible how much benefit alphafold has brought. And all of that from a less than 100million parameters model.
I might be dumb but could they scale it up and make an alphafold 3 with maybe like 10bln params? Would it be a lot better assuming the same training effort is put into it?
If it does, can't biotech companies just go nuts and make a 100bln params internal model and have all the protein structures they want?