2 papers
cs.LG2024
Provable Training for Graph Contrastive Learning
Yue Yu, Xiao Wang, Mengmei Zhang +2
Graph Contrastive Learning (GCL) has emerged as a popular training approach for learning node embeddings from augmented graphs without labels. Despite the key principle that maximi…
physics.chem-ph2024
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
Yinghao Li, Lingkai Kong, Yuanqi Du +4
Large molecular representation models pre-trained on massive unlabeled data have shown great success in predicting molecular properties. However, these models may tend to overfit t…