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Found 2 papers, 1 papers with code
On the Completeness of Invariant Geometric Deep Learning Models
Our results fill the gap in the theoretical power of invariant models, contributing to a rigorous and comprehensive understanding of their capabilities.
Is Distance Matrix Enough for Geometric Deep Learning?
In this work, we first construct families of novel and symmetric geometric graphs that Vanilla DisGNN cannot distinguish even when considering all-pair distances, which greatly expands the existing counterexample families.
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