1 citations · 3 across the 10 of their papers we have counts for
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Extracting Forward Invariant Sets from Neural Network-Based Control Barrier Functions
Goli Vaisi, James Ferlez, Yasser Shoukry
Training Neural Networks (NNs) to serve as Barrier Functions (BFs) is a popular way to improve the safety of autonomous dynamical systems. Despite significant practical success, th…
Polynomial-Time Reachability for LTI Systems with Two-Level Lattice Neural Network Controllers
James Ferlez, Yasser Shoukry
In this paper, we consider the computational complexity of bounding the reachable set of a Linear Time-Invariant (LTI) system controlled by a Rectified Linear Unit (ReLU) Two-Level…
Fast BATLLNN: Fast Box Analysis of Two-Level Lattice Neural Networks
James Ferlez, Haitham Khedr, Yasser Shoukry
In this paper, we present the tool Fast Box Analysis of Two-Level Lattice Neural Networks (Fast BATLLNN) as a fast verifier of box-like output constraints for Two-Level Lattice (TL…
Assured Neural Network Architectures for Control and Identification of Nonlinear Systems
James Ferlez, Yasser Shoukry
In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture (number of layers and number of neurons per layer)…
Safe-by-Repair: A Convex Optimization Approach for Repairing Unsafe Two-Level Lattice Neural Network Controllers
Ulices Santa Cruz, James Ferlez, Yasser Shoukry
In this paper, we consider the problem of repairing a data-trained Rectified Linear Unit (ReLU) Neural Network (NN) controller for a discrete-time, input-affine system. That is we…
Bounding the Complexity of Formally Verifying Neural Networks: A Geometric Approach
James Ferlez, Yasser Shoukry
In this paper, we consider the computational complexity of formally verifying the behavior of Rectified Linear Unit (ReLU) Neural Networks (NNs), where verification entails determi…