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Hamilton-Jacobi (HJ) reachability analysis provides a formal method for guaranteeing safety in constrained control problems.
Safe control under input limits with neural control barrier functions
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Efficient neural network robustness certification with general activation functions
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Neural lyapunov control
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Formal synthesis of lyapunov neural networks
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Automated and formal synthesis of neural barrier certificates for dynamical models
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Beta-crown: Efficient bound propagation with per-neuron split constraints for neural network robustness verification
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First three years of the international verification of neural networks competition (vnn-comp)
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Abate, A., Ahmed, D., Giacobbe, M., and Peruffo, A. (2020) · 2020
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Overcoming the curse of dimensionality for some hamilton–jacobi partial differential equations via neural network architectures
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Nnv: the neural network verification tool for deep neural networks and learning-enabled cyber-physical systems
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Integer programming
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Fast and complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers.
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Deepreach: A deep learning approach to high-dimensional reachability
Bansal, S., and Tomlin, C. J. (2021) · 2021
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The safety filter: A unified view of safety-critical control in autonomous systems
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Simultaneous synthesis and verification of neural control barrier functions through branch-and-bound verification-in-the-loop training.
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Safe model-based reinforcement learning with an uncertainty-aware reachability certificate.
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A minimum discounted reward hamilton–jacobi formulation for computing reachable sets
Akametalu, A. K., Ghosh, S., Fisac, J. F., Rubies-Royo, V., and Tomlin, C. J. (2024) · 2024
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Verification-aided learning of neural network barrier functions with termination guarantees.
Chen, S., Molu, L., and Fazlyab, M. (2024) · 2024
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Agile but safe: Learning collision-free high-speed legged locomotion.
He, T., Zhang, C., Xiao, W., He, G., Liu, C., and Shi, G. (2024) · 2024
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Learning a formally verified control barrier function in stochastic environment.
Tayal, M., Zhang, H., Jagtap, P., Clark, A., and Kolathaya, S. (2024) · 2024
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Improve certified training with signal-to-noise ratio loss to decrease neuron variance and increase neuron stability.
Wei, T., Wang, Z., Niu, P., ABUDUWEILI, A., Zhao, W., Hutchison, C., Sample, E., and Liu, C. (2024) · 2024
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Exact verification of relu neural control barrier functions
Zhang, H., Wu, J., Vorobeychik, Y., and Clark, A. (2024) · 2024
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