Pages that link to "Item:Q5239232"
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The following pages link to Reachability analysis for neural feedback systems using regressive polynomial rule inference (Q5239232):
Displaying 14 items.
- Verisig 2.0: verification of neural network controllers using Taylor model preconditioning (Q832173) (← links)
- An iterative scheme of safe reinforcement learning for nonlinear systems via barrier certificate generation (Q832198) (← links)
- Risk-averse autonomous systems: a brief history and recent developments from the perspective of optimal control (Q2082497) (← links)
- Reachability analysis of a general class of neural ordinary differential equations (Q2112130) (← links)
- State-based confidence bounds for data-driven stochastic reachability using Hilbert space embeddings (Q2123214) (← links)
- Static analysis of ReLU neural networks with tropical polyhedra (Q2145325) (← links)
- Learning safe neural network controllers with barrier certificates (Q5918375) (← links)
- Reluplex: a calculus for reasoning about deep neural networks (Q6108442) (← links)
- Generating probabilistic safety guarantees for neural network controllers (Q6134350) (← links)
- Verifying generalization in deep learning (Q6535549) (← links)
- Hybrid controller synthesis for nonlinear systems subject to reach-avoid constraints (Q6535637) (← links)
- Verification-guided programmatic controller synthesis (Q6536130) (← links)
- Verifying the generalization of deep learning to out-of-distribution domains (Q6611966) (← links)
- Observer-based safety monitoring of nonlinear dynamical systems with neural networks via quadratic constraint approach (Q6646008) (← links)
