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cs.LG2020
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
Fei Ding, Xiaohong Zhang, Justin Sybrandt +1
Graph representation learning based on graph neural networks (GNNs) can greatly improve the performance of downstream tasks, such as node and graph classification. However, the gen…
cs.LG2019
Double cycle-consistent generative adversarial network for unsupervised conditional generation
Fei Ding, Feng Luo, Yin Yang
Conditional generative models have achieved considerable success in the past few years, but usually require a lot of labeled data. Recently, ClusterGAN combines GAN with an encoder…