Can anyone recommend any arXiv/paper links on the subject for someone with reasonable prerequisties (e.g. neural ODEs, physics informed neural networks and message passing)? The number of references in the article is a bit of an overload! Looks like fascinating field.
From a quick glance, the blog post seems to be based on the following paper involving the same author: https://arxiv.org/abs/2106.10934
The point of the post is how graph NNs are limited in capability, so I’m not sure graph NN surveys are the best references (sibling comment) for the main punchline, though they might certainly contain many important details.
The reply is not addressing GP's concern. A survey of the literature of GNN fundamentals will contain a lot of repetition for someone who is already comfortable with message passing and neural networks (particularly neural ODEs) generally.
This is good stuff. One really cool use of sheaf theory is being able to predict race conditions between 2 states in switching electrical circuits by showing H_1 ("line-like", if H_0 is "point-like") cohomology isomorphic (? not sure if that's the right term) to Z_2: https://www.drmichaelrobinson.net/sheaftutorial/20150826_tut... (slide 29)
They do actually exist. Caltech has (had?) a group doing asynchronous microprocessors, which were most naturally expressed as channels between computation units. (They had a subset of CSP they called CHP -- communicating hardware processes that they used for a lot of the high-level designs).
As usual with nice things, the market didn't actually want it. Though Intel did buy a startup they spun out and used it mostly for switch fabric purposes.
Absolutely. Protein folding has been worked on by a large part of the scientific community for a long time and they're not going to look at a breakthrough like that and just ignore it. Pharma companies will be putting significant resources towards this research as well.
Here is an open source implementation of AlphaFold from an academic at Columbia.
my apologies for my language - i was still referring to GNN in general production use.
I do see a lot of cool research coming out ... but i still dont see much in production.
Even alipay uses a plain vanilla word2vec + xgboost for its graph based fraud detection.
Is anyone running anything related to GNN in production ?
this is a critical question because if the tooling isnt available ... all these cool research will actually not go live. And that's what im seeing today.
Not a ding on the researchers...but something to think about.
