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• W3100010556 abstract "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 a general framework to improve graph-based neural network models by combining self-supervised auxiliary learning tasks 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." @default.
• W3100010556 created "2020-11-23" @default.
• W3100010556 creator A5011098514 @default.
• W3100010556 creator A5015018705 @default.
• W3100010556 date "2020-11-14" @default.
• W3100010556 modified "2023-09-25" @default.
• W3100010556 title "Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks" @default.
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• W3100010556 hasPublicationYear "2020" @default.
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