Grain: Improving data efficiency of graph neural networks via diversified influence maximization

W Zhang, Z Yang, Y Wang, Y Shen, Y Li… - arXiv preprint arXiv …, 2021 - arxiv.org
Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …
被引用次数：47 相关文章
[PDF] vldb.org

[PDF][PDF] Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization

W Zhang, Z Yang, Y Wang, Y Shen, Y Li, L Wang, B Cui - vldb.org
Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …

GRAIN: improving data efficiency of graph neural networks via diversified influence maximization

W Zhang, Z Yang, Y Wang, Y Shen, Y Li… - Proceedings of the …, 2021 - dl.acm.org
Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …

Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization

W Zhang, Z Yang, Y Wang, Y Shen, Y Li… - arXiv e …, 2021 - ui.adsabs.harvard.edu
Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …
[PDF] vldb.org

[PDF][PDF] Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization

W Zhang, Z Yang, Y Wang, Y Shen, Y Li, L Wang, B Cui - vldb.org
Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …