9 citations · 17 across the 6 of their papers we have counts for
Showing 2024 · cs.LGShow all
2 papers · 2 filters
cs.LG2024★ 2 cited
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…
cs.LG2024★ 1 cited
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
Manuel Brenner, Elias Weber, Georgia Koppe +1
In science, we are often interested in obtaining a generative model of the underlying system dynamics from observed time series. While powerful methods for dynamical systems recons…