3 papers
cs.CE2026
Cell-JEPA: Latent Representation Learning for Single-Cell Transcriptomics
Ali ElSheikh, Rui-Xi Wang, Weimin Wu +9
Single-cell foundation models learn by reconstructing masked gene expression, implicitly treating technical noise as signal. With dropout rates exceeding 90%, reconstruction object…
cs.AI2025
MedFuse: Multiplicative Embedding Fusion For Irregular Clinical Time Series
Yi-Hsien Hsieh, Ta-Jung Chien, Chun-Kai Huang +2
Clinical time series derived from electronic health records (EHRs) are inherently irregular, with asynchronous sampling, missing values, and heterogeneous feature dynamics. While n…
cs.LG2024
Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning
Chun-Kai Huang, Yi-Hsien Hsieh, Ta-Jung Chien +5
Multivariate time series (MTS) data, when sampled irregularly and asynchronously, often present extensive missing values. Conventional methodologies for MTS analysis tend to rely o…