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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A CNN for LHC beam-loss time-series classification gains up to 18.6% higher robust accuracy via a differentiable preprocessing wrapper and adversarial fine-tuning, with extension to sequence-level temporal robustness.
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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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Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment
A CNN for LHC beam-loss time-series classification gains up to 18.6% higher robust accuracy via a differentiable preprocessing wrapper and adversarial fine-tuning, with extension to sequence-level temporal robustness.