论文标题
基于图的神经网络模型具有多个自制辅助任务
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks
论文作者
论文摘要
自我监督的学习目前正在引起广泛关注，因为它允许神经网络从大量未标记的数据中学习强大的表示。此外，多任务学习可以同时通过培训网络在相关任务上进一步改善表示的表示，从而导致了大幅度的改进。在本文中，我们提出了三个新颖的自学辅助任务，以多任务的方式训练基于图的神经网络模型。由于图形卷积网络是捕获结构化数据点之间关系的最有前途的方法之一，因此我们将它们用作基础，以在标准的半监督图分类任务上获得竞争成果。
Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to learn robust representations from large quantities of unlabeled data. Additionally, multi-task learning can further improve representation learning by training networks simultaneously on related tasks, leading to significant performance improvements. In this paper, we propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion. Since Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points, we use them as a building block to achieve competitive results on standard semi-supervised graph classification tasks.
