COCOCO is a conformal framework for NeSy-CBMs that jointly conformalizes concepts and labels, reconciles them via deduction-abduction revision, and satisfies consistency, coverage, and conciseness while retaining distribution-free guarantees.
The deeplog neurosymbolic machine
2 Pith papers cite this work. Polarity classification is still indexing.
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A neurosymbolic approach uses fuzzy logic constraints to refine SAM under weak supervision, producing improved pseudo-labels that enable state-of-the-art segmentation on Pascal VOC and REFUGE2.
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Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
COCOCO is a conformal framework for NeSy-CBMs that jointly conformalizes concepts and labels, reconciles them via deduction-abduction revision, and satisfies consistency, coverage, and conciseness while retaining distribution-free guarantees.
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Weakly Supervised Segmentation as Semantic-Based Regularization
A neurosymbolic approach uses fuzzy logic constraints to refine SAM under weak supervision, producing improved pseudo-labels that enable state-of-the-art segmentation on Pascal VOC and REFUGE2.