7 citations · 10 across the 7 of their papers we have counts for
7 papers
Bayesian Entropy Neural Networks for Physics-Aware Prediction
Rahul Rathnakumar, Jiayu Huang, Hao Yan +1
This paper addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limi…
Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports
Xinyu Zhao, Hao Yan, Yongming Liu
A large volume of accident reports is recorded in the aviation domain, which greatly values improving aviation safety. To better use those reports, we need to understand the most i…
Curvature Augmented Manifold Embedding and Learning
Yongming Liu
A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed. The key novel contribution is to formulate…
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…
Hulk: Graph Neural Networks for Optimizing Regionally Distributed Computing Systems
Zhengqing Yuan, Huiwen Xue, Chao Zhang +1
Large deep learning models have shown great potential for delivering exceptional results in various applications. However, the training process can be incredibly challenging due to…
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…