17 citations · 23 across the 7 of their papers we have counts for
7 papers
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values
Yu Zheng, Huan Yee Koh, Ming Jin +6
The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial pro…
Less is More: A Closer Look at Semantic-based Few-Shot Learning
Chunpeng Zhou, Haishuai Wang, Xilu Yuan +2
Few-shot Learning aims to learn and distinguish new categories with a very limited number of available images, presenting a significant challenge in the realm of deep learning. Rec…
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
Yihe Wang, Yu Han, Haishuai Wang +1
Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. Howev…
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis
Ke Liu, Feng Liu, Haishuai Wang +3
(INRs) aim to learn a (i.e., a neural network) to represent an image, where the input and output of the fu…
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR
Chunpeng Zhou, Kangjie Ning, Haishuai Wang +3
The application of 3D ground-penetrating radar (3D-GPR) for subgrade distress detection has gained widespread popularity. To enhance the efficiency and accuracy of detection, pione…
hierarchical network with decoupled knowledge distillation for speech emotion recognition
Ziping Zhao, Huan Wang, Haishuai Wang +1
The goal of Speech Emotion Recognition (SER) is to enable computers to recognize the emotion category of a given utterance in the same way that humans do. The accuracy of SER is st…