83 citations · 292 across the 25 of their papers we have counts for
9 papers · 1 filter
Motif-aware Attribute Masking for Molecular Graph Pre-training
Eric Inae, Gang Liu, Meng Jiang
Attribute reconstruction is used to predict node or edge features in the pre-training of graph neural networks. Given a large number of molecules, they learn to capture structural…
A Quantitative Review on Language Model Efficiency Research
Meng Jiang, Hy Dang, Lingbo Tong
Language models (LMs) are being scaled and becoming powerful. Improving their efficiency is one of the core research topics in neural information processing systems. Tay et al. (20…
Semi-Supervised Graph Imbalanced Regression
Gang Liu, Tong Zhao, Eric Inae +2
Data imbalance is easily found in annotated data when the observations of certain continuous label values are difficult to collect for regression tasks. When they come to molecule…
Data-Centric Learning from Unlabeled Graphs with Diffusion Model
Gang Liu, Eric Inae, Tong Zhao +3
Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been collected from various sources and a…
Graph Rationalization with Environment-based Augmentations
Gang Liu, Tong Zhao, Jiaxin Xu +2
Rationale is defined as a subset of input features that best explains or supports the prediction by machine learning models. Rationale identification has improved the generalizabil…
Graph Data Augmentation for Graph Machine Learning: A Survey
Tong Zhao, Wei Jin, Yozen Liu +5
Data augmentation has recently seen increased interest in graph machine learning given its demonstrated ability to improve model performance and generalization by added training da…