论文标题
分子属性预测和图形超网的分类
Molecule Property Prediction and Classification with Graph Hypernetworks
论文作者
论文摘要
图神经网络目前正在领导基于学习的分子属性预测和分类中的性能图。因此，计算化学已成为通用图神经网络以及专门消息传递方法的突出测试。在这项工作中，我们证明，用超网络的替换基础网络会提高性能，从而获得了各种基准。应用超网络的主要困难是它们缺乏稳定性。我们通过组合当前消息和第一条消息来解决此问题。最近的一项工作通过用低阶泰勒近似替换消息传递网络的激活函数来解决错误纠正码的训练不稳定。我们证明我们的通用解决方案可以替代该特定领域的解决方案。
Graph neural networks are currently leading the performance charts in learning-based molecule property prediction and classification. Computational chemistry has, therefore, become the a prominent testbed for generic graph neural networks, as well as for specialized message passing methods. In this work, we demonstrate that the replacement of the underlying networks with hypernetworks leads to a boost in performance, obtaining state of the art results in various benchmarks. A major difficulty in the application of hypernetworks is their lack of stability. We tackle this by combining the current message and the first message. A recent work has tackled the training instability of hypernetworks in the context of error correcting codes, by replacing the activation function of the message passing network with a low-order Taylor approximation of it. We demonstrate that our generic solution can replace this domain-specific solution.
