Search Results for author: Yongbin Feng

Found 3 papers, 1 papers with code

Structural Re-weighting Improves Graph Domain Adaptation

1 code implementation5 Jun 2023 Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiu Qiang, Pan Li

This work examines different impacts of distribution shifts caused by either graph structure or node attributes and identifies a new type of shift, named conditional structure shift (CSS), which current GDA approaches are provably sub-optimal to deal with.

Attribute Domain Adaptation

Semi-supervised Graph Neural Network for Particle-level Noise Removal

no code implementations NeurIPS Workshop AI4Scien 2021 Tianchun Li, Shikun Liu, Yongbin Feng, Nhan Tran, Miaoyuan Liu, Pan Li

The graph neural network is trained on charged particles with well-known labels, which can be obtained from simulation truth information or measurements from data, and inferred on neutral particles of which such labeling is missing.

Cannot find the paper you are looking for? You can Submit a new open access paper.