18 citations · 28 across the 7 of their papers we have counts for
7 papers
Drifting Fields are not Conservative
Leonard T. Franz, Sebastian Hoffmann, Tim Weiland +2
Drifting models have recently gained attention for generating high-quality samples in a single forward pass. During training, they learn a push-forward map by following a vector-va…
SoftJAX & SoftTorch: Empowering Automatic Differentiation Libraries with Informative Gradients
Anselm Paulus, A. René Geist, Vít Musil +3
Automatic differentiation (AD) frameworks such as JAX and PyTorch have enabled gradient-based optimization for a wide range of scientific fields. Yet, many "hard" primitives in the…
AtmoDist: Self-supervised Representation Learning for Atmospheric Dynamics
Sebastian Hoffmann, Christian Lessig
Representation learning has proven to be a powerful methodology in a wide variety of machine learning applications. For atmospheric dynamics, however, it has so far not been consid…
Towards Representation Learning for Atmospheric Dynamics
Sebastian Hoffmann, Christian Lessig
The prediction of future climate scenarios under anthropogenic forcing is critical to understand climate change and to assess the impact of potentially counter-acting technologies.…
LP Formulations of sufficient statistic based strategies in Finite Horizon Two-Player Zero-Sum Stochastic Bayesian games
Nabiha Nasir Orpa, Lichun Li
This paper studies two-player zero-sum stochastic Bayesian games where each player has its own dynamic state that is unknown to the other player. Using typical techniques, we provi…
Optimising Spatial and Tonal Data for PDE-based Inpainting
Laurent Hoeltgen, Markus Mainberger, Sebastian Hoffmann +6
Some recent methods for lossy signal and image compression store only a few selected pixels and fill in the missing structures by inpainting with a partial differential equation (P…