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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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相关实验视频

Updated: Jul 13, 2025

Immunohistochemistry and Multiple Labeling with Antibodies from the Same Host Species to Study Adult Hippocampal Neurogenesis
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拉恩网:通过传播标签来学习强大的GCN.

Chunxu Zhang1, Ximing Li1, Hongbin Pei2

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.

Neural networks : the official journal of the International Neural Network Society
|October 17, 2023
PubMed
概括

本研究介绍了LAbel-ENhanced网络 (LaenNet) 以改善在不完美的图形数据上的图形卷积网络 (GCN). 通过将标签与特征一起传播,LaenNet提高了GCN的稳定性,在杂和稀疏的场景中优于现有的模型.

关键词:
图表 卷积网络 卷积网络标签 标签 标签 标签坚固性 坚固性 坚固性

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科学领域:

  • 图形表示学习学习学习图形表示.
  • 机器学习 机器学习
  • 人工智能的人工智能是人工智能.

背景情况:

  • 图形卷积网络 (GCNs) 对于图形表示学习非常强大,但与现实世界杂和稀疏的数据作斗争.
  • 不完善的图形数据,包括杂/稀疏的特征或标签,挑战了现有的GCN模型的稳定性.
  • 需要GCN架构,可以有效处理数据不完美.

研究的目的:

  • 提出一种新的架构,即 LAbel-ENhanced Networks (LaenNet),以提高 GCN 的稳定性.
  • 为了提高GCN在带有噪音或稀疏特征和标签的图形数据上的性能.
  • 提供一个可通用的模块,可以集成到各种GCN变体中.

主要方法:

  • 引入了LaenNet模块,旨在与GCN内部的功能同时传播标签.
  • 将LaenNet模块集成到GCN的隐藏层中,以将传播的标签与隐藏的表示结合起来.
  • 在半监督节点分类任务上评估LaenNet,使用具有四种类型数据缺陷的数据集:杂特征,稀疏特征,杂标签和稀疏标签.

主要成果:

  • 与最先进的基线模型相比,LaenNet在所有测试的噪音和稀疏图形数据场景中表现出优越的性能和稳定性.
  • 拟议的方法有效地处理图形表示学习中的各种形式的数据缺陷.
  • 经验结果验证了将标签传播集成到 GCN 架构中的有效性.

结论:

  • 在有噪音和稀疏图形数据的情况下,LaenNet提供了一种简单而有效的解决方案,以提高GCN的稳定性.
  • 在LaenNet中的标签传播机制显著提高了在具有挑战性的现实世界图形场景中的性能.
  • LaenNet架构是可泛化的,为图形表示学习领域提供了宝贵的贡献.