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相关概念视频

Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Cluster Sampling Method01:20

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

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Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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相关实验视频

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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通过全球知识引导的节点生成来进行联合子图学习.

Yuxuan Liu1, Zhiming He1, Shuang Wang2

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
概括

联合图形学习 (FGL) 通过生成反映全球数据分布的伪图形节点来提高性能,克服了本地培训的局限性. 这种方法增强了分布式环境中的图形神经网络 (GNN) 模型.

关键词:
深度学习是一种深度学习.分布式学习是一种分布式的学习.联合学习的联合学习图表学习学习图表学习机器学习是机器学习.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 联合图形学习 (FGL) 结合了使用图形神经网络 (GNN) 的图形表示学习和联合学习.
  • 在FGL的本地培训中只使用子图,导致由于缺少信息而导致业绩下降.
  • 现有的子图完成方法可能会引入偏差,因为它们不代表全球图分布.

研究的目的:

  • 引入一种新的联合图形学习方法,MN-FGAGN,以减轻缺少邻居信息引起的性能问题.
  • 为了生成准确地表示全球图分布的伪图节点,克服本地偏见.
  • 在分散的环境中提高GNN的准确性和稳定性.

主要方法:

  • 拟议的MN-FGAGN将生成对抗神经网络 (GAN) 分成客户端的歧视器和服务器端的生成器.
  • 服务器端生成器从所有客户端接收监督信息,以了解全球数据分布.
  • 伪图节点是为了整合全球信息而生成的,以补偿局部缺失的数据.

主要成果:

  • MN-FGAGN通过生成具有全球代表性的伪图节点,有效地减轻缺失邻居信息的影响.
  • 拟议的客户端-区分器和服务器-生成器架构使得生成器能够从分布式数据中学习.
  • 在四个现实世界数据集上的实验表明,与最先进的方法相比,性能优越.

结论:

  • MN-FGAGN通过解决局部子图限制的挑战,为联合图形学习提供了强大的解决方案.
  • 这种方法成功地产生了伪图节点,捕捉了全球图分布,提高了模型性能.
  • 这项工作通过在去中心化环境中实现更准确,更公正的GNN培训,推动了联合图形学习.