Author

Jun Fang

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Conference Open access Sep 2026

Disentangling Coarse and Fine Latent Dynamics for Probabilistic Time Series Forecasting

Probabilistic time series forecasting seeks to quantify the uncertainty of future observations. While recent works introduce latent variables to alleviate the spurious dependencies caused by hidden confounders, thereby reducing overly wide confidence intervals, simply incorporating latent factors is not sufficient. Whe...

Chang-Ze Zhou, Rui-Chu Cai, Sheng Nie et al. · Proceedings of the Thirty-Fi... · 0 citations

#artificial intelligence Preprint Aug 2026

ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning

Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable representation learning, classification still typically requires fitting a task-specific classifier on each target dataset, while individual ch...

Jun Fang, Shi-Feng Xie, Rui-Chu Cai et al. · 0 citations

Preprint Aug 2026

Rethinking Normalization Placement for LLMs: Post-Norm under Curriculum Depth Growing

Pre-norm is the standard normalization placement in modern Transformers because it facilitates joint optimization of full-depth models. We ask whether this preference persists when depth is introduced through a curriculum. In curriculum depth growth, each appended block receives the boundary representation produced by...

Sheng Ren, Yadong Wang, Naiqiang Tan et al. · 0 citations