6 citations · 11 across the 11 of their papers we have counts for
5 papers · 1 filter
veriFIRE: an Industrial Case Study in Verifying Consistency Properties for a DNN-Based Wildfire Detection System
Idan Refaeli, Maya Swisa, Itay Buchnik +5
We present our ongoing work on the veriFIRE project: a collaboration between industry and academia, aimed at applying verification to increase the reliability of a real-world, safe…
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…