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%2C with a particular focus on safety-critical applications. However%2C formal verification techniques still face major scalability and precision challenges. The over-approximation introduced during the formal verification process to tackle the scalability challenge often results in inconclusive analysis. To address this challenge%2C we propose a novel framework to generate Verification-Friendly Neural Networks (VNNs). We present a post-training optimization framework to achieve a balance between preserving prediction performance and verification-friendliness. Our proposed framework results in VNNs that are comparable to the original DNNs in terms of prediction performance%2C while amenable to formal verification techniques. This essentially enables us to establish robustness for more VNNs than their DNN counterparts%2C in a time-efficient manner.?quality=80&w=800)
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