QLL is a novel logic for neuro-symbolic learning that uses ML-native operations (sum, log-sum-exp) on logits to embed constraints, satisfying most linear logic properties and showing stronger correlation between empirical robustness and formal verification than prior approaches.
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Differential halo zonotopes enable static verification of global robustness in DNNs by jointly propagating pairs of perturbed inputs while bounding divergence, with a relaxed confidence-based variant.
Case study verifying a neural network drug-dosing controller for infinite-horizon safety using Rocq and the Vehicle theorem prover.
citing papers explorer
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Quantitative Linear Logic for Neuro-Symbolic Learning and Verification
QLL is a novel logic for neuro-symbolic learning that uses ML-native operations (sum, log-sum-exp) on logits to embed constraints, satisfying most linear logic properties and showing stronger correlation between empirical robustness and formal verification than prior approaches.
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Differential Zonotopes for Verifying Global Robustness of DNNs
Differential halo zonotopes enable static verification of global robustness in DNNs by jointly propagating pairs of perturbed inputs while bounding divergence, with a relaxed confidence-based variant.
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Vancomycert: A Certified Neuro-Symbolic Drug Delivery System (Case Study)
Case study verifying a neural network drug-dosing controller for infinite-horizon safety using Rocq and the Vehicle theorem prover.