Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems
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SwitchPFN introduces a shared projection and regime codebook for time‑series classification with tabular foundation models, preserving local temporal transitions while ensuring consistent feature definitions across samples. The method outperforms existing representations, achieving the highest mean accuracy on evaluated benchmarks and improving the strongest baseline by 4.47% relative. Ablation, sensitivity, and limited‑data experiments confirm the benefits of the proposed representation design.
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Neural PDE solvers provide efficient surrogates for time-dependent physical systems, but autoregressive prediction over long horizons remains challenging because local errors can induce distribution s...