Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability ...

Our proposed architecture achieved promising results by automating the selection of loss and optimization functions, improving the performance of GCN and GAT models. The GCN model outperformed the GAT ...

Traditional loss functions used in SVM, such as the hinge loss and the 0/1 loss, are pivotal for formulating the SVM optimization problem but falter when data is not linearly separable. They also ...