论文标题
图形神经网络表达和分子性质回归的元学习
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression
论文作者
论文摘要
我们证明了模型 - 反应算法对于分子回归任务中GNN模型的元学习算法的适用性。与使用随机初始化的GNN相比，我们可以使用元学习学习新的化学预测任务，而这些任务只有几个模型更新，这些GNN需要从头开始学习每个回归任务。我们通过实验表明，GNN层表达与改善的元学习相关。此外，我们还试验了GNN弹药，以产生最佳性能和快速收敛的K-Hot学习。
We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks. Using meta-learning we are able to learn new chemical prediction tasks with only a few model updates, as compared to using randomly initialized GNNs which require learning each regression task from scratch. We experimentally show that GNN layer expressivity is correlated to improved meta-learning. Additionally, we also experiment with GNN emsembles which yield best performance and rapid convergence for k-shot learning.
