no code implementations • 29 May 2024 • Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann
Motivated by these limitations, we propose Spatio-Spectral Graph Neural Networks (S$^2$GNNs) -- a new modeling paradigm for Graph Neural Networks (GNNs) that synergistically combines spatially and spectrally parametrized graph filters.
no code implementations • 7 Mar 2024 • Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan Günnemann
Differential privacy (DP) has various desirable properties, such as robustness to post-processing, group privacy, and amplification by subsampling, which can be derived independently of each other.
1 code implementation • 8 Mar 2023 • Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann
Neural architectures that learn potential energy surfaces from molecular data have undergone fast improvement in recent years.
1 code implementation • 24 Nov 2022 • Ameya Daigavane, Arthur Kosmala, Miles Cranmer, Tess Smidt, Shirley Ho
Here, we propose an alternative construction for learned physical simulators that are inspired by the concept of action-angle coordinates from classical mechanics for describing integrable systems.