70 citations · 202 across the 12 of their papers we have counts for
6 papers · 1 filter
Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input
Neelanjana Pal, Diego Manzanas Lopez, Taylor T Johnson
Data-driven, neural network (NN) based anomaly detection and predictive maintenance are emerging research areas. NN-based analytics of time-series data offer valuable insights into…
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)
Christopher Brix, Mark Niklas Müller, Stanley Bak +2
This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021,…
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…
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…
Specification-Guided Safety Verification for Feedforward Neural Networks
Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
This paper presents a specification-guided safety verification method for feedforward neural networks with general activation functions. As such feedforward networks are memoryless…
Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations
Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
Neural networks have been widely used to solve complex real-world problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the…