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Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis

AAAI 2020 · Architectures and efficiency › Neural architecture search · approach: Latency-aware architecture search · 114 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Venus (dependency analysis + splitting + ideal cuts) vs NSVerify plain MILPMILP verification · MNISTtotal time: 26,906.81 -> 5,953.46Helped
add dependency cuts to big-M MILP (ablation)Venus · MNISTtotal time: 13,555.49 -> 9,729.07Helped
add ideal cuts to big-M MILP (ablation)Venus · MNISTtotal time: 13,555.49 -> 6,777.95Helped
add input domain splitting on high-dimensional input (ablation)Venus · MNISTtotal time: 13,555.49 -> 11,409.96No clear effect
combine dependency cuts with ideal cuts (ablation)Venus · MNISTtotal time: 6,777.95 -> 5,953.46Helped
dependency analysis callback frequency too high or too low (ablation)Venus · MNISTaverage timeHurt

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