这篇 arXiv 论文用集成共识做分子图半监督学习，不用费力设计数据增强，单个模型就能超过传统全集成，还降校准误差。
该论文（arXiv:2607.28304v1）提出用集成共识目标训练分子图模型，解决分子领域难以设计标签保持增强的问题。实验覆盖多种分子数据集、任务类型和图神经网络架构，显示半监督集成共识能提升预测准确率。单成员模型经此训练后，在几乎所有情况下都优于传统监督方式训练的全集成，并降低校准误差。
Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus
Machine learning is transforming molecular sciences by accelerating property prediction, simulation, and the discovery of new molecules and materials. Acquiring labeled data in these domains is often costly and time-consuming, whereas large collections of unlabeled molecular data are readily available. Standard semi-supervised learning methods often rely on label-preserving augmentations, which are challenging to design in the molecular domain, where minor changes can drastically alter properties. In this work, we show that semi-supervised methods that rely on an ensemble consensus can boost predictive accuracy across a diverse range of molecular datasets, task types, and graph neural network architectures. We find that training with an ensemble consensus objective increases robustness in models and exhibits an effect similar to knowledge distillation; an individual member of an ensemble trained this way outperforms a full ensemble trained in a traditional supervised fashion in almost all cases. In addition, this type of semi-supervised training reduces calibration error.