Constructs k-inductive neural barrier certificates for partially unknown nonlinear dynamics by combining neural networks, a data-driven fundamental lemma from one trajectory, and CEGIS-SMT verification.
IEEE Transactions on information theory , volume=
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α-TCAV replaces TCAV's hard indicator with a tunable smooth function to create a unified probabilistic framework with lower variance and guidance for parameter choice or Bayes-optimal scoring.
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k-Inductive Neural Barrier Certificates for Unknown Nonlinear Dynamics
Constructs k-inductive neural barrier certificates for partially unknown nonlinear dynamics by combining neural networks, a data-driven fundamental lemma from one trajectory, and CEGIS-SMT verification.
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$\alpha$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
α-TCAV replaces TCAV's hard indicator with a tunable smooth function to create a unified probabilistic framework with lower variance and guidance for parameter choice or Bayes-optimal scoring.
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