A distillation method that turns spectral-filter sequence models into explicit linear dynamical systems, enabling constant-time per-token generation with a provable (conditional) accuracy bound.
Learning long-term dependencies with gradient descent is difficult
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SpectraLDS: Provable Distillation for Linear Dynamical Systems
A distillation method that turns spectral-filter sequence models into explicit linear dynamical systems, enabling constant-time per-token generation with a provable (conditional) accuracy bound.