Search Results for author: Yongming Liu

Found 20 papers, 8 papers with code

Curvature Augmented Manifold Embedding and Learning

1 code implementation21 Mar 2024 Yongming Liu

A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed.

Data Visualization Metric Learning +1

Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning

1 code implementation20 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.

Conformal Prediction Graph Learning +1

Reinforcement Learning With Reward Machines in Stochastic Games

no code implementations27 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.

Multi-agent Reinforcement Learning Q-Learning +1

CAMEL: Curvature-Augmented Manifold Embedding and Learning

no code implementations5 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.

Computational Efficiency Dimensionality Reduction +1

Hulk: Graph Neural Networks for Optimizing Regionally Distributed Computing Systems

no code implementations27 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.

Distributed Computing

B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization

no code implementations14 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.

Boundary Detection Management +3

RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding

1 code implementation12 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.

CoLA Natural Language Inference +5

Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints

no code implementations16 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.

Uncertainty Quantification

Neural Optimization Machine: A Neural Network Approach for Optimization

no code implementations8 Aug 2022 Jie Chen, Yongming Liu

The NN objective function can have arbitrary architectures and activation functions.

Multiobjective Optimization

Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning

no code implementations13 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).

Continuous Control Management +2

Robust physics discovery via supervised and unsupervised pattern recognition using the Euler characteristic

1 code implementation15 Oct 2021 Zhiming Zhang, Yongming Liu

Machine learning approaches have been widely used for discovering the underlying physics of dynamical systems from measured data.

BIG-bench Machine Learning

Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential Equations

1 code implementation8 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.

Bayesian Inference Model Selection

Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks

no code implementations17 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.

Decision Making Image Classification

FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction

no code implementations31 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.

ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data

1 code implementation24 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

Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design

1 code implementation22 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.

Dimensionality Reduction

Cannot find the paper you are looking for? You can Submit a new open access paper.