Author

Zhenyi Zhu

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Conference Aug 2026

Deep Switching Koopman Networks: Discovery of Switching Dynamic Regimes in Industrial Systems

Reliable modeling of non-stationary multivariate time series, particularly those arising in industrial monitoring, requires capturing the dynamics that govern sequential observations. Existing CNN- and Transformer-based classifiers often rely on stationary discriminative patterns and therefore do not explicitly represe...

Zhen-Yi Zhu, Ling-Zheng Zhang, Chen Shao et al. · 2026 IEEE 22nd International... · 0 citations

#machine learning Preprint Sep 2026

SwitchPFN: Shared Switching Dynamics for Frozen In-Context Time Series Classification

This work proposes SwitchPFN, which learns a shared projection and regime codebook from the training sequences, making local dynamic operators and transition features directly comparable across samples, and achieves the highest mean accuracy among the evaluated methods.

Zhen-Yi Zhu, Jacqueline Pang, Pei-Lin Shen et al. · 0 citations

#graph neural networks Aug 2026

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series

This work proposes Graph Layer for Inference in Dynamic En- vironments (GLIDE), a novel GNN layer enhanced by two theoretically grounded design mechanisms that significantly improve learning under dynamic topology while preserving robustness in static scenarios.

Chen Shao, Yue Wang, Zhen-Yi Zhu et al. · Lecture notes in computer sc... · 1 citation