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Found 2 papers, 2 papers with code
Graph Representation Learning via Aggregation Enhancement
Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and propagating information between layers, which limits their performance.
Smoothed Inference for Adversarially-Trained Models
In this work, we study the application of randomized smoothing as a way to improve performance on unperturbed data as well as to increase robustness to adversarial attacks.
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