Abstraction-refinement framework with SHAP-guided timestep selection improves certified robustness verification success and margin tightness for RNNs over abstraction-only baselines.
Improved branch and bound for neural network verification via lagrangian decomposition,
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FaVeX accelerates verified explanations for neural networks via dynamic batch-sequential processing and query reuse while introducing verifier-optimal robust explanations that incorporate verifier incompleteness.
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Robustness Verification of Recurrent Neural Networks with Abstraction Refinement
Abstraction-refinement framework with SHAP-guided timestep selection improves certified robustness verification success and margin tightness for RNNs over abstraction-only baselines.
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Faster Verified Explanations for Neural Networks
FaVeX accelerates verified explanations for neural networks via dynamic batch-sequential processing and query reuse while introducing verifier-optimal robust explanations that incorporate verifier incompleteness.