Continual Learning of Dynamical Systems in Recurrent Neural Networks through Recyclable Unit Gating
Dynamical Systems Reconstruction (DSR) aims to infer models from observed time series that reproduce a system's qualitative long-term behavior. Continual DSR (cDSR) requires learning new systems while preserving previously learned dynamics, yet even small parameter updates in recurrent models can qualitatively alter th...
Sima Hashemi, D. Durstewitz, G. Koppe
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