Bi-Lipschitz encoders in neural graph matching provide controlled GED surrogates and better alignment costs, leading to improved prediction and ranking on benchmarks.
How powerful are graph neural networks? InInternational Conference on Learning Representations
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Characterizes monotone separating set functions with dimension bounds, proves non-existence on infinite domains, and introduces a Holder-stable neural model with a weak version of the property for universal monotone approximation.
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Monotone and Separable Set Functions: Characterizations and Neural Models
Characterizes monotone separating set functions with dimension bounds, proves non-existence on infinite domains, and introduces a Holder-stable neural model with a weak version of the property for universal monotone approximation.