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Balancing Augmentation with Edge-Utility Filter for Signed GNNs
Secondly, the original signed graph is selectively augmented with the use of (1) an edge perturbation regulator to balance the number of positive and negative edges and to determine the ratio of perturbed edges to original edges and (2) an edge utility filter to remove the negative edges with low utility to make the graph structure more balanced.
iMedBot: A Web-based Intelligent Agent for Healthcare Related Prediction and Deep Learning
Method: the iMedBot is a web application that we developed using the python Flask web framework and deployed on Amazon Web Services.
Empirical Study of Overfitting in Deep FNN Prediction Models for Breast Cancer Metastasis
We also find some interesting interacting pairs of hyperparameters such as learning rate and momentum, learning rate and decay, and batch size and epochs.
