SENECA uses a novel self-consistent missing mass calculation to improve discrete entropy estimates in small-sample regimes and outperforms alternatives in numerical tests.
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Flash PD-SSM achieves FSA-level expressivity by discretely selecting one matrix from a trainable set of structured sparse transition matrices at each time step while preserving the runtime and memory efficiency of standard structured SSMs.
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SENECA: Small-Sample Discrete Entropy Estimation via Self-Consistent Missing Mass
SENECA uses a novel self-consistent missing mass calculation to improve discrete entropy estimates in small-sample regimes and outperforms alternatives in numerical tests.
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Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models
Flash PD-SSM achieves FSA-level expressivity by discretely selecting one matrix from a trainable set of structured sparse transition matrices at each time step while preserving the runtime and memory efficiency of standard structured SSMs.