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Found 3 papers, 2 papers with code
Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy
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.
Ewald-based Long-Range Message Passing for Molecular Graphs
Neural architectures that learn potential energy surfaces from molecular data have undergone fast improvement in recent years.
Learning Integrable Dynamics with Action-Angle Networks
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.
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