5 citations · 12 across the 15 of their papers we have counts for
6 papers · 1 filter
Euclidean Fourier Neural Operators
Nathanael Bosch, Niklas Frederik Schmitz, Michael F. Herbst
Fourier neural operators (FNOs) provide an efficient framework for learning mappings between function spaces as they are, by construction, independent of the grid resolution at whi…
Multi-layer Stack Ensembles for Time Series Forecasting
Nathanael Bosch, Oleksandr Shchur, Nick Erickson +2
Ensembling is a powerful technique for improving the accuracy of machine learning models, with methods like stacking achieving strong results in tabular tasks. In time series forec…
Low-Rank Filtering and Smoothing for Sequential Deep Learning
Joanna Sliwa, Frank Schneider, Nathanael Bosch +2
Learning multiple tasks sequentially requires neural networks to balance retaining knowledge, yet being flexible enough to adapt to new tasks. Regularizing network parameters is a…
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
Jonas Beck, Nathanael Bosch, Michael Deistler +4
Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging. I…
Fenrir: Physics-Enhanced Regression for Initial Value Problems
Filip Tronarp, Nathanael Bosch, Philipp Hennig
We show how probabilistic numerics can be used to convert an initial value problem into a Gauss--Markov process parametrised by the dynamics of the initial value problem. Consequen…
Planning from Images with Deep Latent Gaussian Process Dynamics
Nathanael Bosch, Jan Achterhold, Laura Leal-Taixé +1
Planning is a powerful approach to control problems with known environment dynamics. In unknown environments the agent needs to learn a model of the system dynamics to make plannin…