4 papers
A Transition System Abstraction Framework for Neural Network Dynamical System Models
Yejiang Yang, Zihao Mo, Hoang-Dung Tran +1
This paper proposes a transition system abstraction framework for neural network dynamical system models to enhance the model interpretability, with applications to complex dynamic…
Compression Repair for Feedforward Neural Networks Based on Model Equivalence Evaluation
Zihao Mo, Yejiang Yang, Shuaizheng Lu +1
In this paper, we propose a method of repairing compressed Feedforward Neural Networks (FNNs) based on equivalence evaluation of two neural networks. In the repairing framework, a…
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…
A Data-Driven Hybrid Automaton Framework to Modeling Complex Dynamical Systems
Yejiang Yang, Zihao Mo, Weiming Xiang
In this paper, a computationally efficient data-driven hybrid automaton model is proposed to capture unknown complex dynamical system behaviors using multiple neural networks. The…