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.
Zico Kolter , title =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.