27 citations · 39 across the 7 of their papers we have counts for
8 papers
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding
Zhilu Wang, Chao Huang, Qi Zhu
The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sens…
Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner
Xiangguo Liu, Chao Huang, Yixuan Wang +2
Neural networks have shown great promises in planning, control, and general decision making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving performan…
Verification in the Loop: Correct-by-Construction Control Learning with Reach-avoid Guarantees
Yixuan Wang, Chao Huang, Zhaoran Wang +2
In the current control design of safety-critical autonomous systems, formal verification techniques are typically applied after the controller is designed to evaluate whether the r…
Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation
Yixuan Wang, Chao Huang, Zhilu Wang +3
Neural networks are being increasingly applied to control and decision-making for learning-enabled cyber-physical systems (LE-CPSs). They have shown promising performance without r…
Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems
Yixuan Wang, Chao Huang, Qi Zhu
Neural networks have been increasingly applied for control in learning-enabled cyber-physical systems (LE-CPSs) and demonstrated great promises in improving system performance and…
SAW: A Tool for Safety Analysis of Weakly-hard Systems
Chao Huang, Kai-Chieh Chang, Chung-Wei Lin +1
We introduce SAW, a tool for safety analysis of weakly-hard systems, in which traditional hard timing constraints are relaxed to allow bounded deadline misses for improving design…