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Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
[article]
2020
arXiv
pre-print
Deep multi-task learning attracts much attention in recent years as it achieves good performance in many applications. Feature learning is important to deep multi-task learning for sharing common information among tasks. In this paper, we propose a Hierarchical Graph Neural Network (HGNN) to learn augmented features for deep multi-task learning. The HGNN consists of two-level graph neural networks. In the low level, an intra-task graph neural network is responsible of learning a powerful
arXiv:2002.04813v1
fatcat:tajh3bxyibec5cp2g7ohzdconu