no code implementations • 18 Feb 2024 • Yejiang Yang, Zihao Mo, Hoang-Dung Tran, Weiming Xiang
This paper proposes a transition system abstraction framework for neural network dynamical system models to enhance the model interpretability, with applications to complex dynamical systems such as human behavior learning and verification.
no code implementations • 18 Feb 2024 • Zihao Mo, Yejiang Yang, Shuaizheng Lu, Weiming Xiang
Based on the computed output discrepancy, the repairing method first initializes a new training set for the compressed networks to narrow down the discrepancy between the two neural networks and improve the performance of the compressed network.
no code implementations • 26 Apr 2023 • Yejiang Yang, Zihao Mo, Weiming Xiang
Then, a collection of small-scale neural networks that are computationally efficient are trained as the local dynamical description for their corresponding topologies.
no code implementations • 27 Jul 2021 • 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.