Search Results for author: Hoang-Dung Tran

Found 11 papers, 4 papers with code

A Transition System Abstraction Framework for Neural Network Dynamical System Models

no code implementations18 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.

Neural Network Repair with Reachability Analysis

no code implementations9 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.

Collision Avoidance

Reachability Analysis of Convolutional Neural Networks

no code implementations22 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.

Reachable Set Estimation for Neural Network Control Systems: A Simulation-Guided Approach

no code implementations26 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.

Verification of Deep Convolutional Neural Networks Using ImageStars

2 code implementations12 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.

Image Classification Pose Estimation +1

NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems

no code implementations12 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.

Reachability Analysis for Feed-Forward Neural Networks using Face Lattices

1 code implementation2 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.

Specification-Guided Safety Verification for Feedforward Neural Networks

1 code implementation14 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.

Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations

no code implementations21 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.

Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks

no code implementations9 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.

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