works on

From the 1 of 9 linked papers with an AI index.

activity
20172021
most citedReachable Set Computation and Safety Verification for Neural Networks with ReLU Activations

70 citations · 98 across the 7 of their papers we have counts for

collaborators

9 papers

cs.LG2021

Neural Network Repair with Reachability Analysis

Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran +3

Safety is a critical concern for the next generation of autonomy that is likely to rely heavily on deep neural networks for perception and control. Formally verifying the safety an…

cs.CV20212 cited

Reachability Analysis of Convolutional Neural Networks

Xiaodong Yang, Tomoya Yamaguchi, Hoang-Dung Tran +3

Deep convolutional neural networks have been widely employed as an effective technique to handle complex and practical problems. However, one of the fundamental problems is the lac…

cs.LG2020

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…

eess.SY20204 cited

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

Weiming Xiang, Hoang-Dung Tran, Xiaodong Yang +1

The vulnerability of artificial intelligence (AI) and machine learning (ML) against adversarial disturbances and attacks significantly restricts their applicability in safety-criti…

eess.SY20201 cited

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

Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez +5

This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical syst…

cs.AI202011 cited

Reachability Analysis for Feed-Forward Neural Networks using Face Lattices

Xiaodong Yang, Hoang-Dung Tran, Weiming Xiang +1

The paper introduces a parallelizable method that uses face lattices to compute exact reachable sets for feed‑forward ReLU neural networks, enabling more efficient safety verificat…