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Found 2 papers, 2 papers with code
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
In many scientific disciplines, we are interested in inferring the nonlinear dynamical system underlying a set of observed time series, a challenging task in the face of chaotic behavior and noise.
On the difficulty of learning chaotic dynamics with RNNs
Here we offer a comprehensive theoretical treatment of this problem by relating the loss gradients during RNN training to the Lyapunov spectrum of RNN-generated orbits.
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