866 citations · 1.8k across the 14 of their papers we have counts for
14 papers
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Tianxin Wei, Yuning You, Tianlong Chen +3
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (w…
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations
Yuning You, Tianlong Chen, Zhangyang Wang +1
Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on doma…
Graph Contrastive Learning Automated
Yuning You, Tianlong Chen, Yang Shen +1
Self-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs. Among many, g…
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Yue Cao, Payel Das, Vijil Chenthamarakshan +3
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering. Challenges exist due to the complex sequence--fold…
Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction
Yuning You, Yang Shen
Compound-protein pairs dominate FDA-approved drug-target pairs and the prediction of compound-protein affinity and contact (CPAC) could help accelerate drug discovery. In this stud…
Graph Contrastive Learning with Augmentations
Yuning You, Tianlong Chen, Yongduo Sui +3
Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been develop…