Gating in RNNs couples state time-scales with parameter gradients to produce lag- and direction-dependent effective learning rates, shown via exact Jacobians and first-order expansion.
Long expressive memory for sequence modeling
2 Pith papers cite this work. Polarity classification is still indexing.
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Process-Informed Forecasting models incorporating deterministic production recipe priors outperform ARIMA and deep learning baselines in accuracy, physical plausibility, and noise resilience for temperature forecasting in pharmaceutical lyophilization.
citing papers explorer
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Time-Scale Coupling Between States and Parameters in Recurrent Neural Networks
Gating in RNNs couples state time-scales with parameter gradients to produce lag- and direction-dependent effective learning rates, shown via exact Jacobians and first-order expansion.
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Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
Process-Informed Forecasting models incorporating deterministic production recipe priors outperform ARIMA and deep learning baselines in accuracy, physical plausibility, and noise resilience for temperature forecasting in pharmaceutical lyophilization.