Search Results for author:
Found 2 papers, 1 papers with code
Graph Contrastive Learning for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
Improving Covariance-Regularized Discriminant Analysis for EHR-based Predictive Analytics of Diseases
In this paper, we revised the problem of predictive analysis of disease using personal EHR data and LDA classifier.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
