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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3 Pith papers cite this work, alongside 13 external citations. Polarity classification is still indexing.
3
Pith papers citing it
13
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years
2026 3verdicts
UNVERDICTED 3representative citing papers
Adapts TP and FSDP to bound-propagation verification, with FSDP delivering bitwise-identical bounds and 80-90% baseline memory reduction while TP trades some tightness for ~2x peak-memory savings.
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.
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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Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism
Adapts TP and FSDP to bound-propagation verification, with FSDP delivering bitwise-identical bounds and 80-90% baseline memory reduction while TP trades some tightness for ~2x peak-memory savings.
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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.