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相关概念视频

Vector Algebra: Graphical Method01:10

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and...
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相关实验视频

Updated: May 28, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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生成和对比的图表表示学习与传递信息的学习.

Ying Tang1, Yining Yang1, Guodao Sun1

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310000, Zhejiang, China.

Neural networks : the official journal of the International Neural Network Society
|February 8, 2025
PubMed
概括
此摘要是机器生成的。

自主监督图表表示学习 (SSGRL) 结合了生成和对比方法,以获得更好的图表嵌入. 我们的新型对比生成信息传递图学习 (CGMP-GL) 增强了节点表示的可区分性和模型的稳定性.

关键词:
相反的学习学习.图形自动编码器的自动编码器传递信息的传递自主监督的图表表示学习学习.

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科学领域:

  • 机器学习 机器学习
  • 图形神经网络 图形神经网络
  • 人工智能的人工智能

背景情况:

  • 自主监督图形表示学习 (SSGRL) 避免了图形嵌入的手动标签.
  • 现有的SSGRL方法要么是生成的 (容易导致质量差),要么是对比的 (对增强和负样本敏感).
  • 无论是纯粹的生成方法还是对比方法都不能充分平衡稳健性和性能.

研究的目的:

  • 提出一种新的SSGRL方法,即对比生成信息传递图形学习 (CGMP-GL).
  • 整合生成式和对比式学习范式,以改善图形表示.
  • 为了提高节点表示的可区分性和模型的稳定性.

主要方法:

  • CGMP-GL 将对比式学习集成到生成模型和消息聚合中.
  • 采用交叉视图多层对比,以确保多颗粒度拓和特征信息.
  • 通过自我监督的对比性消息传递重建掩盖节点功能并优化表示.

主要成果:

  • CGMP-GL通过对正面样本进行对齐和分离负面样本来证明增强的歧视性.
  • 该方法有效地整合了不同细分度的拓和特征信息.
  • 广泛的实验证实了CGMP-GL在多个数据集和任务上的有效性和稳定性.

结论:

  • CGMP-GL提供了一种强大而有效的自主监督图表表示学习方法.
  • 综合生成和对比策略克服了现有方法的局限性.
  • CGMP-GL显著提高了各种下游图表任务的性能.