BriefGPT.xyz
Jun, 2020
跳跃采样:适用于非平稳环境的简单正则化图学习
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments
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Young-Jin Park, Kyuyong Shin, Kyung-Min Kim
TL;DR
这篇论文讨论了在面对非平稳的数据环境时,如何通过使用抽样方式避免过拟合以提升图表示学习的性能并加以应用于推荐系统,实验结果显示该方法能够显著提高GNN模型的预测准确性并减轻平滑过度问题。
Abstract
graph representation learning
is gaining popularity in a wide range of applications, such as social networks analysis, computational biology, and
recommender systems
. However, different with positive results from
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