Qimai Li

Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semi-supervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph …

Label Efficient Semi-Supervised Learning via Graph Filtering

2019

Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and unlabeled data samples to improve learning performance. However, existing graph-based …