3 papers
cs.LG2026
Uncovering the Computational Roles of Nonlinearity in Sequence Modeling Using Almost-Linear RNNs
Manuel Brenner, Georgia Koppe
Sequence modeling tasks across domains such as natural language processing, time series forecasting, and control require learning complex input-output mappings. Nonlinear recurrenc…
cs.LG2025
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…
cs.LG2024
Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction
Manuel Brenner, Christoph Jürgen Hemmer, Zahra Monfared +1
Dynamical systems (DS) theory is fundamental for many areas of science and engineering. It can provide deep insights into the behavior of systems evolving in time, as typically des…