87 citations · 350 across the 30 of their papers we have counts for
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cs.LG2019★ 87 cited
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou, Ziniu Hu, Yewen Wang +3
Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a…
cs.CL2019★ 7 cited
Few-Shot Representation Learning for Out-Of-Vocabulary Words
Ziniu Hu, Ting Chen, Kai-Wei Chang +1
Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately…
cs.LG2019★ 24 cited
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
Ziniu Hu, Changjun Fan, Ting Chen +2
Graph neural networks (GNNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GNN model requires a large collection of label…