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Found 2 papers, 2 papers with code
Stateful ODE-Nets using Basis Function Expansions
The recently-introduced class of ordinary differential equation networks (ODE-Nets) establishes a fruitful connection between deep learning and dynamical systems.
Lipschitz Recurrent Neural Networks
Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-understood linear component plus a Lipschitz nonlinearity.
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