91 citations · 280 across the 22 of their papers we have counts for
8 papers · 1 filter
Guaranteed Quantization Error Computation for Neural Network Model Compression
Wesley Cooke, Zihao Mo, Weiming Xiang
Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. The guaranteed output error computa…
Safety Verification of Neural Network Control Systems Using Guaranteed Neural Network Model Reduction
Weiming Xiang, Zhongzhu Shao
This paper aims to enhance the computational efficiency of safety verification of neural network control systems by developing a guaranteed neural network model reduction method. F…
Approximate Bisimulation Relations for Neural Networks and Application to Assured Neural Network Compression
Weiming Xiang, Zhongzhu Shao
In this paper, we propose a concept of approximate bisimulation relation for feedforward neural networks. In the framework of approximate bisimulation relation, a novel neural netw…
Robust Optimization Framework for Training Shallow Neural Networks Using Reachability Method
Yejiang Yang, Weiming Xiang
In this paper, a robust optimization framework is developed to train shallow neural networks based on reachability analysis of neural networks. To characterize noises of input data…
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…