9 citations · 9 across the 4 of their papers we have counts for
5 papers
Data-Driven Hamiltonian for Direct Construction of Safe Set from Trajectory Data
Jason J. Choi, Christopher A. Strong, Koushil Sreenath +2
In continuous-time optimal control, evaluating the Hamiltonian requires solving a constrained optimization problem using the system's dynamics model. Hamilton-Jacobi reachability a…
ZoPE: A Fast Optimizer for ReLU Networks with Low-Dimensional Inputs
Christopher A. Strong, Sydney M. Katz, Anthony L. Corso +1
Deep neural networks often lack the safety and robustness guarantees needed to be deployed in safety critical systems. Formal verification techniques can be used to prove input-out…
Verification of Image-based Neural Network Controllers Using Generative Models
Sydney M. Katz, Anthony L. Corso, Christopher A. Strong +1
Neural networks are often used to process information from image-based sensors to produce control actions. While they are effective for this task, the complex nature of neural netw…
Generating Probabilistic Safety Guarantees for Neural Network Controllers
Sydney M. Katz, Kyle D. Julian, Christopher A. Strong +1
Neural networks serve as effective controllers in a variety of complex settings due to their ability to represent expressive policies. The complex nature of neural networks, howeve…
Global Optimization of Objective Functions Represented by ReLU Networks
Christopher A. Strong, Haoze Wu, Aleksandar Zeljić +4
Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failur…