70 citations · 193 across the 16 of their papers we have counts for
5 papers · 1 filter
Neural Network Repair with Reachability Analysis
Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran +3
Safety is a critical concern for the next generation of autonomy that is likely to rely heavily on deep neural networks for perception and control. Formally verifying the safety an…
Verification of Deep Convolutional Neural Networks Using ImageStars
Hoang-Dung Tran, Stanley Bak, Weiming Xiang +1
Convolutional Neural Networks (CNN) have redefined the state-of-the-art in many real-world applications, such as facial recognition, image classification, human pose estimation, an…
Specification-Guided Safety Verification for Feedforward Neural Networks
Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
This paper presents a specification-guided safety verification method for feedforward neural networks with general activation functions. As such feedforward networks are memoryless…
Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations
Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
Neural networks have been widely used to solve complex real-world problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the…
Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks
Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
In this paper, the output reachable estimation and safety verification problems for multi-layer perceptron neural networks are addressed. First, a conception called maximum sensiti…