MIT researchers designed a computationally efficient algorithm for machine learning with symmetric data that also requires fewer data for training than conventional approaches. Their work could inform ...

Inspired by the work of Victorian mathematician Mary Everest Boole, try making a symmetric curve using string and some ...

Recently, the semi-supervised graph convolutional network (SSGCN) has been verified effective for hyperspectral image (HSI) classification. However, constrained by the limited training data and ...

Graph convolutional networks (GCNs) are fundamental graph neural networks used for solving node classification problems in graph-structured data. GCNs have been reported to be vulnerable to ...

In the construction of QSAR models for the prediction of molecular activity, feature selection is a common task aimed at improving the results and understanding of the problem. The selection of ...