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.
https://arxiv.org/abs/2401.14461
4 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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representative 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.
Introduces the Adversarial Rate metric and associated tools to systematically evaluate and visualize the impact of adversarial inputs on DRL policies using formal verification.
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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Analyzing Adversarial Inputs in Deep Reinforcement Learning
Introduces the Adversarial Rate metric and associated tools to systematically evaluate and visualize the impact of adversarial inputs on DRL policies using formal verification.
- The Luna Bound Propagator for Formal Analysis of Neural Networks