Search Results for author: Christopher A. Strong

Found 4 papers, 1 papers with code

ZoPE: A Fast Optimizer for ReLU Networks with Low-Dimensional Inputs

no code implementations9 Jun 2021 Christopher A. Strong, Sydney M. Katz, Anthony L. Corso, Mykel J. Kochenderfer

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

no code implementations14 May 2021 Sydney M. Katz, Anthony L. Corso, Christopher A. Strong, Mykel J. Kochenderfer

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

1 code implementation1 Mar 2021 Sydney M. Katz, Kyle D. Julian, Christopher A. Strong, Mykel J. Kochenderfer

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

no code implementations7 Oct 2020 Christopher A. Strong, Haoze Wu, Aleksandar Zeljić, Kyle D. Julian, Guy Katz, Clark Barrett, Mykel J. Kochenderfer

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.

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