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Can anyone recommend any arXiv/paper links on the subject for someone with reasonable prerequisties (e.g. neural ODEs, physics informed neural networks and mess
by phonebucket 4y ago
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.
- andreyk 4y agoA survey paper is usually a good way to go, such as A comprehensive survey on graph neural networks (https://arxiv.org/abs/1901.00596 https://arxiv.org/abs/1901.00596) or Graph Neural Networks: A Review of Methods and Applications (https://arxiv.org/abs/1812.08434 https://arxiv.org/abs/1812.08434)
- albertzeyer 4y agoCan someone explain the downvotes here? If you think these are bad papers, maybe recommend some better ones? Or what is wrong with this post?
- uoaei 4y agoThe 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.
- ssivark 4y agoFrom a quick glance, the blog post seems to be based on the following paper involving the same author: https://arxiv.org/abs/2106.10934 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.