122 citations · 154 across the 7 of their papers we have counts for
10 papers
STONet: A Neural-Operator-Driven Spatio-temporal Network
Haitao Lin, Guojiang Zhao, Lirong Wu +1
Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great exp…
SemiRetro: Semi-template framework boosts deep retrosynthesis prediction
Zhangyang Gao, Cheng Tan, Lirong Wu +1
Recently, template-based (TB) and template-free (TF) molecule graph learning methods have shown promising results to retrosynthesis. TB methods are more accurate using pre-encoded…
Git: Clustering Based on Graph of Intensity Topology
Zhangyang Gao, Haitao Lin, Cheng Tan +2
\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clusteri…
Co-learning: Learning from Noisy Labels with Self-supervision
Cheng Tan, Jun Xia, Lirong Wu +1
Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degra…
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction
Lirong Wu, Haitao Lin, Zhangyang Gao +2
Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on the assumption that node…
Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive
Lirong Wu, Haitao Lin, Zhangyang Gao +2
Deep learning on graphs has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the massive and carefully labeled data. However, precis…