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Found 3 papers, 2 papers with code
Representing Outcome-driven Higher-order Dependencies in Graphs of Disease Trajectories
The widespread application of machine learning techniques to biomedical data has produced many new insights into disease progression and improving clinical care.
Deep Ensembles for Graphs with Higher-order Dependencies
Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure.
Predicting Terrorist Attacks in the United States using Localized News Data
From a machine learning perspective, we found that the Random Forest model outperformed several deep models on our multimodal, noisy, and imbalanced data set, thus demonstrating the efficacy of our novel feature representation method in such a context.
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