1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Uncertainty and Structure in Neural Ordinary Differential Equations
Katharina Ott, Michael Tiemann, Philipp Hennig
Neural ordinary differential equations (ODEs) are an emerging class of deep learning models for dynamical systems. They are particularly useful for learning an ODE vector field fro…
cs.LG2023★ 1 cited
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch +2
Modeling an unknown dynamical system is crucial in order to predict the future behavior of the system. A standard approach is training recurrent models on measurement data. While t…