4 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
sbi reloaded: a toolkit for simulation-based inference workflows
Jan Boelts, Michael Deistler, Manuel Gloeckler +30
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a…
Inferring stochastic low-rank recurrent neural networks from neural data
Matthijs Pals, A Erdem Sağtekin, Felix Pei +2
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should idea…
A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science
Sebastian Bischoff, Alana Darcher, Michael Deistler +18
Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, prote…