10 citations · 15 across the 6 of their papers we have counts for
7 papers
Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties
Yutian Pang, Peng Zhao, Jueming Hu +1
This paper addresses aircraft delays, emphasizing their impact on safety and financial losses. To mitigate these issues, an innovative machine learning (ML)-enhanced landing schedu…
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning
Yutian Pang, Jueming Hu, Christopher S. Lieber +2
Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The…
B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization
Rahul Rathnakumar, Yutian Pang, Yongming Liu
Accurately detecting crack boundaries is crucial for reliability assessment and risk management of structures and materials, such as structural health monitoring, diagnostics, prog…
Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints
Jiayu Huang, Yutian Pang, Yongming Liu +1
Neural Networks (NNs) have been widely {used in supervised learning} due to their ability to model complex nonlinear patterns, often presented in high-dimensional data such as imag…
ProspectNet: Weighted Conditional Attention for Future Interaction Modeling in Behavior Prediction
Yutian Pang, Zehua Guo, Binnan Zhuang
Behavior prediction plays an important role in integrated autonomous driving software solutions. In behavior prediction research, interactive behavior prediction is a less-explored…
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines
Jueming Hu, Zhe Xu, Weichang Wang +3
In multi-agent reinforcement learning (MARL), it is challenging for a collection of agents to learn complex temporally extended tasks. The difficulties lie in computational complex…
Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks
Yutian Pang, Sheng Cheng, Jueming Hu +1
To evaluate the robustness gain of Bayesian neural networks on image classification tasks, we perform input perturbations, and adversarial attacks to the state-of-the-art Bayesian…