Looped SSMs with shared parameters across depth match or exceed standard SSMs with more parameters on time series classification, with additional gains from input reshaping techniques.
Parallelization of non-linear state-space models: Scaling up liquid-resistance liquid-capacitance networks for efficient sequence modeling
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Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification
Looped SSMs with shared parameters across depth match or exceed standard SSMs with more parameters on time series classification, with additional gains from input reshaping techniques.