4 citations · 9 across the 10 of their papers we have counts for
5 papers · 1 filter
Verifying Safety of Neural Networks from Topological Perspectives
Zhen Liang, Dejin Ren, Bai Xue +3
Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their beha…
Repairing Deep Neural Networks Based on Behavior Imitation
Zhen Liang, Taoran Wu, Changyuan Zhao +4
The increasing use of deep neural networks (DNNs) in safety-critical systems has raised concerns about their potential for exhibiting ill-behaviors. While DNN verification and test…
Credit Assignment for Trained Neural Networks Based on Koopman Operator Theory
Zhen Liang, Changyuan Zhao, Wanwei Liu +3
Credit assignment problem of neural networks refers to evaluating the credit of each network component to the final outputs. For an untrained neural network, approaches to tackling…
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
Renjue Li, Hanwei Zhang, Pengfei Yang +4
In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks. The framework builds an ensemble of sub-models -…
Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning
Renjue Li, Pengfei Yang, Cheng-Chao Huang +3
To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with sc…