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Common defaults in actor-critic algorithms can lead to unreliable performance, while bounded distributions with adaptive updates prove to be significantly more robust.
By dynamically integrating graph-derived functional connectivity with Riemannian manifold learning, RepSPD unlocks more robust and generalizable EEG representations.
By modeling EEG dynamics in continuous time with Neural ODEs, ODEBrain avoids error accumulation and captures instantaneous, nonlinear brain activity better than recurrent architectures.
Suppressing non-stationary frequencies in time series data yields surprisingly large gains in forecasting accuracy and computational efficiency.
