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.
1 code implementation • 17 Jan 2022 • Stanley Bak, Hoang-Dung Tran
Analysis of this system has spurred a significant body of research in the formal methods community on neural network verification.
no code implementations • 9 Aug 2021 • Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T Johnson, Danil Prokhorov
Formally verifying the safety and robustness of well-trained DNNs and learning-enabled systems under attacks, model uncertainties, and sensing errors is essential for safe autonomy.
no code implementations • 22 Jun 2021 • Xiaodong Yang, Tomoya Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T Johnson, Danil Prokhorov
Besides the computation of reachable sets, our approach is also capable of backtracking to the input domain given an output reachable set.
no code implementations • 26 Apr 2020 • Weiming Xiang, Hoang-Dung Tran, Xiaodong Yang, Taylor T. Johnson
Then, in combination with reachability methods developed for various dynamical system classes modeled by ordinary differential equations, a recursive algorithm is developed for over-approximating the reachable set of the closed-loop system.
2 code implementations • 12 Apr 2020 • Hoang-Dung Tran, Stanley Bak, Weiming Xiang, Taylor T. Johnson
Set-based analysis methods can detect or prove the absence of bounded adversarial attacks, which can then be used to evaluate the effectiveness of neural network training methodology.
no code implementations • 12 Apr 2020 • Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson
For learning-enabled CPS, such as closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis schemes for linear plant models and FFNN controllers with piecewise-linear activation functions, such as ReLUs.
1 code implementation • 2 Mar 2020 • Xiaodong Yang, Hoang-Dung Tran, Weiming Xiang, Taylor Johnson
To address this challenge, we propose a parallelizable technique to compute exact reachable sets of a neural network to an input set.
1 code implementation • 14 Dec 2018 • Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
As such feedforward networks are memoryless, they can be abstractly represented as mathematical functions, and the reachability analysis of the neural network amounts to interval analysis problems.
no code implementations • 21 Dec 2017 • Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the output behaviors of neural networks will be crucial for their applications in safety-critical systems. In this paper, the output reachable set computation and safety verification problems for a class of neural networks consisting of Rectified Linear Unit (ReLU) activation functions are addressed.
no code implementations • 9 Aug 2017 • 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.