17 citations · 24 across the 5 of their papers we have counts for
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cs.LG2022★ 6 cited
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
Manuel Brenner, Florian Hess, Jonas M. Mikhaeil +4
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 be…
cs.LG2021★ 17 cited
On the difficulty of learning chaotic dynamics with RNNs
Jonas M. Mikhaeil, Zahra Monfared, Daniel Durstewitz
Recurrent neural networks (RNNs) are wide-spread machine learning tools for modeling sequential and time series data. They are notoriously hard to train because their loss gradient…