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Found 5 papers, 1 papers with code
Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach
Third, we use the upper bounds of the Lipschitz constants and the upper bounds of the tight inclusion functions to design two algorithms for the training and robustness verification of implicit neural networks.
Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks
We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs).
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach
First, given an implicit neural network, we introduce a related embedded network and show that, given an $\ell_\infty$-norm box constraint on the input, the embedded network provides an $\ell_\infty$-norm box overapproximation for the output of the given network.
Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems
An important quality of an RTA system is that the assurance mechanism is constructed in a way that is entirely agnostic to the underlying structure of the primary controller.
Improving the Fidelity of Mixed-Monotone Reachable Set Approximations via State Transformations
Mixed-monotone systems are separable via a decomposition function into increasing and decreasing components, and this decomposition function allows for embedding the system dynamics in a higher-order monotone embedding system.
