3 papers
cs.LG2026
Detecting Invariant Manifolds in ReLU-Based RNNs
Lukas Eisenmann, Alena Brändle, Zahra Monfared +1
Recurrent Neural Networks (RNNs) have found widespread applications in machine learning for time series prediction and dynamical systems reconstruction, and experienced a recent re…
cs.LG2026
Continuous-Time Piecewise-Linear Recurrent Neural Networks
Alena Brändle, Alena Brändle, Lukas Eisenmann +3
In dynamical systems reconstruction (DSR) we aim to recover the dynamical system (DS) underlying observed time series. Specifically, we aim to learn a generative surrogate model wh…
cs.LG2024
A scalable generative model for dynamical system reconstruction from neuroimaging data
Eric Volkmann, Alena Brändle, Daniel Durstewitz +1
Data-driven inference of the generative dynamics underlying a set of observed time series is of growing interest in machine learning and the natural sciences. In neuroscience, such…