2 citations · 3 across the 4 of their papers we have counts for
6 papers
Seeing Time: Visual-Temporal Representation Learning for Interpretable Time Series Clustering
Zheng Zhu, Zexi Tan, Yuming Deng +1
Multivariate Time Series (MTS) clustering is an important tool in temporal data mining, aiming to discover latent group structures from complex observations without supervision. Al…
AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
Tao Xie, Zexi Tan, Haoyi Xiao +5
Multivariate time series classification (MTSC) is pivotal in high-stakes domains, such as clinical diagnosis and industrial fault detection, where safe deployment necessitates tran…
Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers
Yiqun Zhang, Zexi Tan, Xiaopeng Luo +1
Most real-world IoT data analysis tasks, such as clustering and anomaly event detection, are unsupervised and highly susceptible to the presence of outliers. In addition to sporadi…
Mask the Redundancy: Evolving Masking Representation Learning for Multivariate Time-Series Clustering
Zexi Tan, Xiaopeng Luo, Yunlin Liu +1
Multivariate Time-Series (MTS) clustering discovers intrinsic grouping patterns of temporal data samples. Although time-series provide rich discriminative information, they also co…
DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification
Tao Xie, Zexi Tan, Haoyi Xiao +2
Early Time Series Classification (ETSC) is critical in time-sensitive medical applications such as sepsis, yet it presents an inherent trade-off between accuracy and earliness. Thi…
MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction
Zexi Tan, Tao Xie, Binbin Sun +3
Sepsis is a life-threatening infectious syndrome associated with high mortality in intensive care units (ICUs). Early and accurate sepsis prediction (SP) is critical for timely int…