activity
20192026
most citedSUOD: Toward Scalable Unsupervised Outlier Detection

9 citations · 23 across the 9 of their papers we have counts for

collaborators

11 papers

stat.ML2026

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…

cs.CV2022

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…

cs.LG20221 cited

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…

cs.CL2022

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…

eess.SP20224 cited

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…

cs.LG20229 cited

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…