Summary
New research suggests recurrent networks can adapt online more efficiently without complex Jacobian propagation. This is achieved by leveraging temporal credit in the forward pass and using immediate derivatives, while also avoiding stale trace memory and normalizing gradient scales. An architectural rule predicts when gradient normalization is crucial, potentially simplifying online learning across various RNN architectures.
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