Jul 6, 2022 · We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU, sigmoid, or hyperbolic tangent ...

Jun 3, 2023 · We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU, sigmoid, or hyperbolic tangent ...

In particular, we abstract the input-output relation of each neuron by a polynomial ap- proximation, which is evaluated in a set-based manner using polynomial.

We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU, sigmoid, or hyperbolic ...

We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU, sigmoid, or hyperbolic ...

In [14] , polynomial zonotopes are used to abstract the closed-loop dynamics, providing tight overapproximations. Other approaches include the use of polynomial ...

Abstract. We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU,.

This capsule reproduces the results presented in the paper "Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopes".

Jul 6, 2022 · Our proposed method is especially well suited for reachability analysis of neural network controlled systems since polynomial zonotopes are able ...

Sep 30, 2023 · Bibliographic details on Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopes.