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…
Model Predictive Control with Reach-avoid Analysis
Dejin Ren, Wanli Lu, Jidong Lv +2
In this paper we investigate the optimal controller synthesis problem, so that the system under the controller can reach a specified target set while satisfying given constraints.…
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…
Provable Reach-avoid Controllers Synthesis Based on Inner-approximating Controlled Reach-avoid Sets
Jianqiang Ding, Taoran Wu, Yuping Qian +2
In this paper, we propose an approach for synthesizing provable reach-avoid controllers, which drive a deterministic system operating in an unknown environment to safely reach a de…
Scenario Approach for Parametric Markov Models
Ying Liu, Andrea Turrini, Moritz Hahn +2
In this paper, we propose an approximating framework for analyzing parametric Markov models. Instead of computing complex rational functions encoding the reachability probability a…