T1 uses one-to-one channel-head binding in a CNN-Transformer hybrid to achieve robust multivariate time-series imputation, cutting average MSE by 46% versus the next-best baseline across 11 datasets even at 70% missingness.
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KNN imputation gives highest photo-z accuracy under ideal random missingness with complete training data, while SAITS is more robust for incomplete training sets and realistic mixed missingness patterns in CSST data.
SPLICE couples JEPA-based latent diffusion with adaptive conformal inference to deliver accurate time-series inpainting with 93-95% empirical coverage on load datasets.
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T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
T1 uses one-to-one channel-head binding in a CNN-Transformer hybrid to achieve robust multivariate time-series imputation, cutting average MSE by 46% versus the next-best baseline across 11 datasets even at 70% missingness.
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Comparative analysis of missing data imputation methods for CSST survey: Impact on photometric redshift estimation performance
KNN imputation gives highest photo-z accuracy under ideal random missingness with complete training data, while SAITS is more robust for incomplete training sets and realistic mixed missingness patterns in CSST data.
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SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting
SPLICE couples JEPA-based latent diffusion with adaptive conformal inference to deliver accurate time-series inpainting with 93-95% empirical coverage on load datasets.