240 citations · 445 across the 20 of their papers we have counts for
6 papers · 1 filter
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance
Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu +4
In this paper, we study contrastive learning from an optimization perspective, aiming to analyze and address a fundamental issue of existing contrastive learning methods that eithe…
Knowledge Embedding Based Graph Convolutional Network
Donghan Yu, Yiming Yang, Ruohong Zhang +1
Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN). How to effectively leverage the rich structural information in complex graph…
Graph-Revised Convolutional Network
Donghan Yu, Ruohong Zhang, Zhengbao Jiang +2
Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linka…
Active Learning for Graph Neural Networks via Node Feature Propagation
Yuexin Wu, Yichong Xu, Aarti Singh +2
Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structu…
Analogical Inference for Multi-Relational Embeddings
Hanxiao Liu, Yuexin Wu, Yiming Yang
Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable sol…
Review Networks for Caption Generation
Zhilin Yang, Ye Yuan, Yuexin Wu +2
We propose a novel extension of the encoder-decoder framework, called a review network. The review network is generic and can enhance any existing encoder- decoder model: in this p…