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Found 4 papers, 1 papers with code
ZoPE: A Fast Optimizer for ReLU Networks with Low-Dimensional Inputs
We demonstrate how to formulate and solve three types of optimization problems: (i) minimization of any convex function over the output space, (ii) minimization of a convex function over the output of two networks in series with an adversarial perturbation in the layer between them, and (iii) maximization of the difference in output between two networks.
Verification of Image-based Neural Network Controllers Using Generative Models
For this reason, recent work has focused on combining techniques in formal methods and reachability analysis to obtain guarantees on the closed-loop performance of neural network controllers.
Generating Probabilistic Safety Guarantees for Neural Network Controllers
In this work, we develop a method to use the results from neural network verification tools to provide probabilistic safety guarantees on a neural network controller.
Global Optimization of Objective Functions Represented by ReLU Networks
However, individual "yes or no" questions cannot answer qualitative questions such as "what is the largest error within these bounds"; the answers to these lie in the domain of optimization.
