FO2 groundings can require 2^Ω(n) DNNF size, but a type-based compiler with residual caching often yields smaller circuits and faster runtimes than naive grounding.
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A neurosymbolic model augments Swin Transformers with focal sets and fuzzy logic to produce calibrated hierarchical image classifications that respect logical constraints.
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On Knowledge Compilation For Two-Variable First-Order Logic
FO2 groundings can require 2^Ω(n) DNNF size, but a type-based compiler with residual caching often yields smaller circuits and faster runtimes than naive grounding.
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A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification
A neurosymbolic model augments Swin Transformers with focal sets and fuzzy logic to produce calibrated hierarchical image classifications that respect logical constraints.