[HN Gopher] The Most Cited AI Papers in 2022
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The Most Cited AI Papers in 2022
Author : marban
Score : 32 points
Date : 2023-03-05 15:46 UTC (7 hours ago)
(HTM) web link (www.zeta-alpha.com)
(TXT) w3m dump (www.zeta-alpha.com)
| elektor wrote:
| I'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.
| godelski wrote:
| 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.
| jltsiren wrote:
| 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.
| antegamisou wrote:
| Probably the only subfield of Computer Science, maybe even
| academic research in general, where citations number is the most
| poor metric of paper quality. The whole situation is more akin to
| mass media reporting the same breakthrough in cancer cure
| research (in mice).
| bryanrasmussen wrote:
| It looks like it might be more interesting on what industries or
| problem domains those most cited AI papers were focused.
| choppaface wrote:
| 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.
| panabee wrote:
| very 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
| usgroup wrote:
| Did nothing of note happen outside of deep learning the whole
| year?
| mtcrawshaw wrote:
| 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.
| tomohelix wrote:
| Incredible 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?
| tiedieconderoga wrote:
| Has there been much research into the idea of distributing
| these large models across many heterogeneous machines?
|
| I'm wondering if there could be a path towards a mix of
| alphafold and folding@home, with donated idle compute resources
| being used to train/run the models.
|
| Designing for that sort of fragmentation could also make it
| easier to slowly run oversized models on local machines with
| swapped memory.
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