31 citations · 90 across the 20 of their papers we have counts for
21 papers
A Functional SVD Framework for Regularized Multivariate Functional PCA with Dual Penalization
Yue Zhao, Hossein Haghbin, Rebecca Sanders +1
This paper introduces a novel framework for Regularized Multivariate Functional Principal Component Analysis (ReMFPCA) via Functional Singular Value Decomposition (SVD). The propos…
Accurate 3D Prediction of Missing Teeth in Diverse Patterns for Precise Dental Implant Planning
Lei Ma, Peng Xue, Yuning Gu +4
In recent years, the demand for dental implants has surged, driven by their high success rates and esthetic advantages. However, accurate prediction of missing teeth for precise di…
Regularized Multivariate Functional Principal Component Analysis
Hossein Haghbin, Yue Zhao, Mehdi Maadooliat
Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional…
Construction of unbiased dental template and parametric dental model for precision digital dentistry
Lei Ma, Jingyang Zhang, Ke Deng +8
Dental template and parametric dental models are important tools for various applications in digital dentistry. However, constructing an unbiased dental template and accurate param…
Stabilization with Prescribed Instant via Lyapunov Method
Jiyuan Kuang, Yabin Gao, Yizhuo Sun +4
This letter investigates the prescribed-instant stabilization problem for high-order integrator systems. In anothor word, the settling time under the presented controller is indepe…
Online Kernel Sliced Inverse Regression
Wenquan Cui, Yue Zhao, Jianjun Xu +1
Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel p…
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression
Wenquan Cui, Yue Zhao, Jianjun Xu +1
Federated learning has become a popular tool in the big data era nowadays. It trains a centralized model based on data from different clients while keeping data decentralized. In t…
EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation
Yue Zhao, Wei Zhang, Tiejun Li
We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibr…
Offline Supervised Learning V.S. Online Direct Policy Optimization: A Comparative Study and A Unified Training Paradigm for Neural Network-Based Optimal Feedback Control
Yue Zhao, Jiequn Han
This work is concerned with solving neural network-based feedback controllers efficiently for optimal control problems. We first conduct a comparative study of two prevalent approa…
Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks
Haodong Duan, Yue Zhao, Kai Chen +2
Deep learning models have achieved excellent recognition results on large-scale video benchmarks. However, they perform poorly when applied to videos with rare scenes or objects, p…
Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis
Yufeng Shi, Shuhuang Chen, Xinge You +3
Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneo…
Clues Before Answers: Generation-Enhanced Multiple-Choice QA
Zixian Huang, Ao Wu, Jiaying Zhou +3
A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it train…
Gaussian Kernel Variance For an Adaptive Learning Method on Signals Over Graphs
Yue Zhao, Ender Ayanoglu
This paper discusses a special kind of a simple yet possibly powerful algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning method predicting unknown nodal…
Learning Robust Representation through Graph Adversarial Contrastive Learning
Jiayan Guo, Shangyang Li, Yue Zhao +1
Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matr…
BA-Net: Bridge Attention for Deep Convolutional Neural Networks
Yue Zhao, Junzhou Chen, Zirui Zhang +1
In recent years, channel attention mechanism has been widely investigated due to its great potential in improving the performance of deep convolutional neural networks (CNNs) in ma…
GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising
Kecheng Chen, Jiayu Sun, Jiang Shen +8
Being low-level radiation exposure and less harmful to health, low-dose computed tomography (LDCT) has been widely adopted in the early screening of lung cancer and COVID-19. LDCT…
Representations of Degenerate Affine Hecke Algebra of Type Under the Etingof-Freund-Ma Functor
Yue Zhao
We compute the image of a polynomial -module under the Etingof-Freund-Ma functor \cite{EFM}. We give a combinatorial description of the image in terms of standard tableaux on…
On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks
Yue Zhao, Yuwei Wu, Caihua Chen +1
While deep learning in 3D domain has achieved revolutionary performance in many tasks, the robustness of these models has not been sufficiently studied or explored. Regarding the 3…
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Yue Zhao, Xiyang Hu, Cheng Cheng +11
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and…
SUOD: Toward Scalable Unsupervised Outlier Detection
Yue Zhao, Xueying Ding, Jianing Yang +1
Outlier detection is a key field of machine learning for identifying abnormal data objects. Due to the high expense of acquiring ground truth, unsupervised models are often chosen…
Extracting clinical concepts from user queries
Yue Zhao, John Handley
Clinical concept extraction often begins with clinical Named Entity Recognition (NER). Often trained on annotated clinical notes, clinical NER models tend to struggle with tagging…