787 citations · 1.3k across the 15 of their papers we have counts for
22 papers
IDPG: An Instance-Dependent Prompt Generation Method
Zhuofeng Wu, Sinong Wang, Jiatao Gu +4
Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage. It freezes the pre-trained…
GRAND+: Scalable Graph Random Neural Networks
Wenzheng Feng, Yuxiao Dong, Tinglin Huang +4
Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate s…
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs
Xiao Liu, Haoyun Hong, Xinghao Wang +4
Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its de…
EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs
Namyong Park, Fuchen Liu, Purvanshi Mehta +3
How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestam…
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
Qinkai Zheng, Xu Zou, Yuxiao Dong +5
Adversarial attacks on graphs have posed a major threat to the robustness of graph machine learning (GML) models. Naturally, there is an ever-escalating arms race between attackers…
A Self-supervised Method for Entity Alignment
Xiao Liu, Haoyun Hong, Xinghao Wang +4
Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing large-scale KGs. Over the course of its…