1 code implementation • 9 Nov 2023 • Philipp Dahlinger, Niklas Freymuth, Michael Volpp, Tai Hoang, Gerhard Neumann
Movement primitives further allow us to accommodate various types of context data, as demonstrated through the utilization of point clouds during inference.
1 code implementation • 31 Oct 2023 • Philipp Dahlinger, Philipp Becker, Maximilian Hüttenrauch, Gerhard Neumann
Before each update, it solves the trust region problem for an optimal step size, resulting in a more stable and faster optimization process.
1 code implementation • NeurIPS 2023 • Niklas Freymuth, Philipp Dahlinger, Tobias Würth, Simon Reisch, Luise Kärger, Gerhard Neumann
Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enabling a favorable trade-off between computational speed and simulation accuracy.
1 code implementation • 23 Sep 2022 • Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann
The two currently most effective methods for GMM-based variational inference, VIPS and iBayes-GMM, both employ independent natural gradient updates for the individual components and their weights.
no code implementations • 29 Sep 2021 • Oleg Arenz, Zihan Ye, Philipp Dahlinger, Gerhard Neumann
Effective approaches for Gaussian variational inference are MORE, VOGN, and VON, which are zero-order, first-order, and second-order, respectively.