GTF-DEER augments the DEER framework with Generalized Teacher Forcing to allow effective parallel training of nonlinear recurrent models on extremely long sequences, improving dynamical systems reconstruction for data with long time scales.
Cambridge University Press
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WassersteinGrad aggregates perturbed gradient attribution maps via their entropic Wasserstein barycenter to avoid blurring from geometric shifts in explanations of autoregressive weather forecasts.
citing papers explorer
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Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction
GTF-DEER augments the DEER framework with Generalized Teacher Forcing to allow effective parallel training of nonlinear recurrent models on extremely long sequences, improving dynamical systems reconstruction for data with long time scales.
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Explanation of Dynamic Physical Field Predictions using WassersteinGrad: Application to Autoregressive Weather Forecasting
WassersteinGrad aggregates perturbed gradient attribution maps via their entropic Wasserstein barycenter to avoid blurring from geometric shifts in explanations of autoregressive weather forecasts.