The output error from fully convex-relaxed neural network verification grows exponentially with depth and linearly with input radius, with misclassification probability showing step-like dependence on radius.
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The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
The output error from fully convex-relaxed neural network verification grows exponentially with depth and linearly with input radius, with misclassification probability showing step-like dependence on radius.
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