Presents Evolving Abstract Transformers with UPOSE and AGG algorithms to create adaptable, domain-agnostic sound transformers for polyhedral abstract domains in program analysis.
Input-relational verification of deep neural networks.Proc
3 Pith papers cite this work, alongside 9 external citations. Polarity classification is still indexing.
3
Pith papers citing it
9
external citations · OpenAlex
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UNVERDICTED 3representative citing papers
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.
Introduces formal verification to compute certified neuron range bounds for CKKS-encrypted neural networks, eliminating overflow failures that previously reached 47%.
citing papers explorer
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Evolving Abstract Transformers for Gradient-Guided, Adaptable Abstract Interpretation
Presents Evolving Abstract Transformers with UPOSE and AGG algorithms to create adaptable, domain-agnostic sound transformers for polyhedral abstract domains in program analysis.
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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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Encrypted Neural Networks without Overflows
Introduces formal verification to compute certified neuron range bounds for CKKS-encrypted neural networks, eliminating overflow failures that previously reached 47%.