1 citations · 1 across the 7 of their papers we have counts for
7 papers
Inner-approximate Reachability Computation via Zonotopic Boundary Analysis
Dejin Ren, Zhen Liang, Chenyu Wu +3
Inner-approximate reachability analysis involves calculating subsets of reachable sets, known as inner-approximations. This analysis is crucial in the fields of dynamic systems ana…
UR4NNV: Neural Network Verification, Under-approximation Reachability Works!
Zhen Liang, Taoran Wu, Ran Zhao +5
Recently, formal verification of deep neural networks (DNNs) has garnered considerable attention, and over-approximation based methods have become popular due to their effectivenes…
An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks
Ye Tao, Wanwei Liu, Fu Song +3
Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage cos…
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…
Safety Verification for Neural Networks Based on Set-boundary Analysis
Zhen Liang, Dejin Ren, Wanwei Liu +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…