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Found 2 papers, 1 papers with code
Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation
This study focuses on long-term forecasting (LTF) on continuous-time dynamic graph networks (CTDGNs), which is important for real-world modeling.
Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation
Since our method reduces the cost for element-wise function computation, it is more efficient than existing cryptographic methods.
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