41 citations · 44 across the 3 of their papers we have counts for
4 papers
Improving Local Effectiveness for Global robust training
Jingyue Lu, M. Pawan Kumar
Despite its popularity, deep neural networks are easily fooled. To alleviate this deficiency, researchers are actively developing new training strategies, which encourage models th…
Neural Network Branch-and-Bound for Neural Network Verification
Florian Jaeckle, Jingyue Lu, M. Pawan Kumar
Many available formal verification methods have been shown to be instances of a unified Branch-and-Bound (BaB) formulation. We propose a novel machine learning framework that can b…
Neural Network Branching for Neural Network Verification
Jingyue Lu, M. Pawan Kumar
Formal verification of neural networks is essential for their deployment in safety-critical areas. Many available formal verification methods have been shown to be instances of a u…
Branch and Bound for Piecewise Linear Neural Network Verification
Rudy Bunel, Jingyue Lu, Ilker Turkaslan +3
The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. In this context…