Summary of Distinguished in Uniform: Self Attention Vs. Virtual Nodes, by Eran Rosenbluth et al.
Distinguished In Uniform: Self Attention Vs. Virtual Nodes
by Eran Rosenbluth, Jan Tönshoff, Martin Ritzert, Berke Kisin, Martin Grohe
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel family of graph transformers, including SAN and GPS, is introduced, which combines message-passing GNNs with global self-attention. These models have been shown to be universal function approximators, although they require initial node features to be augmented with specific positional encodings. Additionally, the approximation is non-uniform, meaning that graphs of varying sizes may require distinct approximating networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph Transformers are new models that can process graph data. They’re like super smart computers that can learn about relationships between things in a graph. These models are really good at learning and can even approximate any function. However, they need some extra information to start with and the way they work changes depending on the size of the graph. |
Keywords
» Artificial intelligence » Self attention
