PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction

arXiv:2310.00614v2 Announce Type: replace
Abstract: Molecular property prediction (MPP) plays a crucial role in biomedical applications, but it often encounters challenges due to a scarcity of labeled data. Existing works commonly adopt gradient-based strategy to update a large amount of parameters for task-level adaptation. However, the increase of adaptive parameters can lead to overfitting and poor performance. Observing that graph neural network (GNN) performs well as both encoder and predictor, we propose PACIA, a parameter-efficient GNN adapter for few-shot MPP. …

abstract adapter applications arxiv biomedical challenges cs.ai cs.lg data few-shot gradient however parameters prediction property role strategy type update