no code implementations • 26 Mar 2024 • Xinyu Zhao, Hao Yan, Yongming Liu
This article argues that we can identify the events more accurately by leveraging the event taxonomy.
1 code implementation • 21 Mar 2024 • Yongming Liu
A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed.
1 code implementation • 27 Nov 2023 • Yutian Pang, Peng Zhao, Jueming Hu, Yongming Liu
This paper addresses aircraft delays, emphasizing their impact on safety and financial losses.
1 code implementation • 20 Jul 2023 • Yutian Pang, Jueming Hu, Christopher S. Lieber, Nancy J. Cooke, Yongming Liu
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.
no code implementations • 27 May 2023 • Jueming Hu, Jean-Raphael Gaglione, Yanze Wang, Zhe Xu, Ufuk Topcu, Yongming Liu
We develop an algorithm called Q-learning with reward machines for stochastic games (QRM-SG), to learn the best-response strategy at Nash equilibrium for each agent.
no code implementations • 5 Mar 2023 • Nan Xu, Yongming Liu
CAMEL utilizes a topology metric defined on the Riemannian manifold, and a unique Riemannian metric for both distance and curvature to enhance its expressibility.
no code implementations • 27 Feb 2023 • Zhengqing Yuan, Huiwen Xue, Chao Zhang, Yongming Liu
Large deep learning models have shown great potential for delivering exceptional results in various applications.
no code implementations • 14 Feb 2023 • 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, prognostics, and maintenance scheduling.
no code implementations • 8 Feb 2023 • Zhengqing Yuan, Huiwen Xue, Chao Zhang, Yongming Liu
EvoText enables the model to learn up-to-date knowledge through a self-escalation process that builds on a priori knowledge.
1 code implementation • 12 Dec 2022 • Zhengqing Yuan, Xiaolong Zhang, Yue Wang, Xuecong Hou, Huiwen Xue, Zhuanzhe Zhao, Yongming Liu
However, existing data augmentation techniques in natural language understanding (NLU) may not fully capture the complexity of natural language variations, and they can be challenging to apply to large datasets.
no code implementations • 16 Oct 2022 • Jiayu Huang, Yutian Pang, Yongming Liu, Hao Yan
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 images and text.
no code implementations • 8 Aug 2022 • Jie Chen, Yongming Liu
The NN objective function can have arbitrary architectures and activation functions.
no code implementations • 13 Nov 2021 • Jueming Hu, Xuxi Yang, Weichang Wang, Peng Wei, Lei Ying, Yongming Liu
Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic Management (UTM).
1 code implementation • 15 Oct 2021 • Zhiming Zhang, Yongming Liu
Machine learning approaches have been widely used for discovering the underlying physics of dynamical systems from measured data.
no code implementations • 30 Sep 2021 • Jueming Hu, Zhe Xu, Weichang Wang, Guannan Qu, Yutian Pang, Yongming Liu
Experimental results show that local information is sufficient for DGRM and agents can accomplish complex tasks with the help of RM.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 8 Jul 2021 • Zhiming Zhang, Yongming Liu
Compared with the conventional Sparse Bayesian Learning (SBL) method, the PeSBL method promotes parsimony of the learned model in addition to its sparsity.
no code implementations • 17 Jun 2021 • Yutian Pang, Sheng Cheng, Jueming Hu, Yongming Liu
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 neural networks, with a benchmark CNN model as reference.
no code implementations • 31 Jan 2020 • Houpu Yao, Yi Gao, Yongming Liu
Based on this, a special type of deep convolutional neural network (DCNN) is proposed that takes advantage of our prior knowledge in physics to build data-driven models whose architectures are of physics meaning.
1 code implementation • 24 Apr 2018 • Jun Wu, Jingrui He, Yongming Liu
Then, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, in which context sampling uses VDRW and the label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs in different classes.
Social and Information Networks
1 code implementation • 22 Dec 2016 • Ruijin Cang, Yaopengxiao Xu, Shaohua Chen, Yongming Liu, Yang Jiao, Max Yi Ren
Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation.