A dual cross-entropy and KL-divergence loss lets recurrent networks maintain stable accuracy over very long streams without hidden-state resets, matching and sometimes slightly beating periodic reset baselines.
Surrogate gradient learning in spiking neural networks,
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Never Reset Again: A Mathematical Framework for Continual Inference in Recurrent Neural Networks
A dual cross-entropy and KL-divergence loss lets recurrent networks maintain stable accuracy over very long streams without hidden-state resets, matching and sometimes slightly beating periodic reset baselines.