1 citations · 1 across the 4 of their papers we have counts for
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Proof Minimization in Neural Network Verification
Omri Isac, Idan Refaeli, Haoze Wu +2
The widespread adoption of deep neural networks (DNNs) requires efficient techniques for verifying their safety. DNN verifiers are complex tools, which might contain bugs that coul…
Abstraction-Based Proof Production in Formal Verification of Neural Networks
Yizhak Yisrael Elboher, Omri Isac, Guy Katz +2
Modern verification tools for deep neural networks (DNNs) increasingly rely on abstraction to scale to realistic architectures. In parallel, proof production is becoming a critical…
PICID: Proof-Driven Clause Learning in Neural Network Verification
Omri Isac, Idan Refaeli, Haoze Wu +2
Current Deep Neural Network (DNN) verifiers are typically designed to prioritize scalability over reliability. Reliability can be reinforced through the generation of proofs that a…
A Certified Proof Checker for Deep Neural Network Verification in Imandra
Remi Desmartin, Omri Isac, Grant Passmore +3
Recent advances in the verification of deep neural networks (DNNs) have opened the way for a broader usage of DNN verification technology in many application areas, including safet…