Proposes pointwise Riemannian Dimension from feature eigenvalues to derive tighter, representation-aware generalization bounds for deep networks in the nonlinear regime.
Advances in neural information processing systems , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
HiTaB introduces a hierarchical Taylor bound framework for neural network reachability that systematically exploits second-order smoothness and curvature Lipschitz constants via layerwise propagation.
LightCROWN computes tighter Jacobian bounds for neural networks with smooth nonlinear activations by exploiting their analytical properties, raising verification success rates for neural control barrier functions up to 100% on benchmark control systems.
citing papers explorer
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Pointwise Generalization in Deep Neural Networks
Proposes pointwise Riemannian Dimension from feature eigenvalues to derive tighter, representation-aware generalization bounds for deep networks in the nonlinear regime.
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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.
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Efficient Verification of Neural Control Barrier Functions with Smooth Nonlinear Activations
LightCROWN computes tighter Jacobian bounds for neural networks with smooth nonlinear activations by exploiting their analytical properties, raising verification success rates for neural control barrier functions up to 100% on benchmark control systems.