Research paper analyzes the theoretical error bounds that result from using convex relaxations in neural network verification. The work proves that the approximation error grows exponentially with network depth and linearly with input radius, with supporting experiments on standard benchmarks.
Research
The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
Theoretical analysis reveals that convex relaxation techniques—widely used to verify neural networks—accumulate approximation errors exponentially with network depth, fundamentally limiting their viability for larger models.
Wednesday, April 22, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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