HiTaB introduces a hierarchical Taylor bound framework for neural network reachability that systematically exploits second-order smoothness and curvature Lipschitz constants via layerwise propagation.
International Conference on Tools and Algorithms for the Construction and Analysis of Systems , pages=
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Hierarchical End-to-End Taylor Bounds for Complete Neural Network Verification
HiTaB introduces a hierarchical Taylor bound framework for neural network reachability that systematically exploits second-order smoothness and curvature Lipschitz constants via layerwise propagation.