2 citations · 3 across the 9 of their papers we have counts for
10 papers
Continual Learning of Dynamical Systems in Recurrent Neural Networks through Recyclable Unit Gating
Sima Hashemi, Daniel Durstewitz, Georgia Koppe
Dynamical Systems Reconstruction (DSR) aims to infer models from observed time series that reproduce a system's qualitative long-term behavior. Continual DSR (cDSR) requires learni…
Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction
Georg Trede, Charlotte Ricarda Doll, Elias Weber +1
Predicting the behavior of dynamical systems (DS) beyond the dynamical and parameter regimes observed in training is a pivotal and essentially unresolved problem in scientific ML.…
The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling
Andre Herz, Matthijs Pals, Daniel Durstewitz +1
Dynamical systems reconstruction (DSR) aims to learn surrogate models that capture the dynamics underlying time-series data. Reliably deploying these surrogates requires uncertaint…
Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction
Florian Hess, Florian Götz, Daniel Durstewitz
Reconstructing nonlinear dynamical systems (DS) from data (DSR) is a fundamental challenge in science and engineering, but it inherently relies on sequential models. Recent breakth…
Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling
Daniel Durstewitz, Christoph Jürgen Hemmer, Florian Hess +2
Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models. With a lot of hype and industrial dema…
Continuous-Time Piecewise-Linear Recurrent Neural Networks
Alena Brändle, Lukas Eisenmann, Florian Götz +1
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…
What Neuroscience Can Teach AI About Learning in Continuously Changing Environments
Daniel Durstewitz, Bruno Averbeck, Georgia Koppe
Modern AI models, such as large language models, are usually trained once on a huge corpus of data, potentially fine-tuned for a specific task, and then deployed with fixed paramet…
dsLassoCov: a federated machine learning approach incorporating covariate control
Han Cao, Augusto Anguita, Charline Warembourg +8
Machine learning has been widely adopted in biomedical research, fueled by the increasing availability of data. However, integrating datasets across institutions is challenging due…
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…
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…