Extends equilibrium propagation to skew-gradient Fitzhugh-Nagumo systems and derives an explicit layer-wise Hamiltonian recurrence for inference in deep residual topologies.
Biophysical journal , volume=
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LeapTS reformulates forecasting as adaptive multi-horizon scheduling via hierarchical control and NCDEs, delivering at least 7.4% better performance and 2.6-5.3x faster inference than Transformer baselines while adapting to non-stationary dynamics.
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Equilibrium Propagation and Hamiltonian Inference in the Diffusive Fitzhugh-Nagumo Model
Extends equilibrium propagation to skew-gradient Fitzhugh-Nagumo systems and derives an explicit layer-wise Hamiltonian recurrence for inference in deep residual topologies.
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LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
LeapTS reformulates forecasting as adaptive multi-horizon scheduling via hierarchical control and NCDEs, delivering at least 7.4% better performance and 2.6-5.3x faster inference than Transformer baselines while adapting to non-stationary dynamics.