Spiking attention is a universal approximator of permutation-equivariant functions with ε-approximation requiring Ω(L_f² nd / ε²) spikes, but low effective dimensions (47-89) allow T=4 timesteps in practice.
URL https://www.sciencedirect.com/science/ article/pii/S0022000004000406
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Establishes a standardized Common Task Framework with three multi-scale seismic wavefield datasets and metrics to enable rigorous head-to-head evaluation of ML methods for reconstruction, forecasting, and generalization.
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Closing the Theory-Practice Gap in Spiking Transformers via Effective Dimension
Spiking attention is a universal approximator of permutation-equivariant functions with ε-approximation requiring Ω(L_f² nd / ε²) spikes, but low effective dimensions (47-89) allow T=4 timesteps in practice.
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The Seismic Wavefield Common Task Framework
Establishes a standardized Common Task Framework with three multi-scale seismic wavefield datasets and metrics to enable rigorous head-to-head evaluation of ML methods for reconstruction, forecasting, and generalization.