6 citations · 8 across the 5 of their papers we have counts for
5 papers · 1 filter
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation
Haitham Khedr, Yasser Shoukry
Formal certification of Neural Networks (NNs) is crucial for ensuring their safety, fairness, and robustness. Unfortunately, on the one hand, sound and complete certification algor…
BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic
Wael Fatnassi, Haitham Khedr, Valen Yamamoto +1
In this paper, we present BERN-NN as an efficient tool to perform bound propagation of Neural Networks (NNs). Bound propagation is a critical step in wide range of NN model checker…
CertiFair: A Framework for Certified Global Fairness of Neural Networks
Haitham Khedr, Yasser Shoukry
We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. Individual Fairness suggests that similar individuals with respect to a certain…
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…
PEREGRiNN: Penalized-Relaxation Greedy Neural Network Verifier
Haitham Khedr, James Ferlez, Yasser Shoukry
Neural Networks (NNs) have increasingly apparent safety implications commensurate with their proliferation in real-world applications: both unanticipated as well as adversarial mis…