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Partial Optimal Transport (POT) extends the classical optimal transport problem by relaxing the strict mass conservation constraint, enabling its use in a wide range of real-world applications. In many of these settings, sparse transport plans are preferred for their interpretability and computational benefits. While s...
Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup that underfits heterogeneous regimes. Replacing the head with a mixture of experts is the standard upgrade, but on instance-normalized backbones (the dominant TSFM design...
The Regime-Enhanced Intelligent Granger Network is proposed, a novel causal discovery framework specifically engineered for nonstationary time series and achieves an Optimal F1 score of 0.529, substantially outperforming PCMCI+ and PC in structural edge recovery.
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