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相关概念视频

Associative Learning01:27

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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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Observational Learning01:12

Observational Learning

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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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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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Introduction to Learning01:18

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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多关系图形对比学习与可学习的图形增强.

Xian Mo1, Jun Pang2, Binyuan Wan3

  • 1School of Information Engineering, Ningxia University, Yinchuan 750021, China; Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Ningxia University, Yinchuan 750021, China.

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

本研究介绍了一种新的多关系图对比学习 (MRGCL) 架构,用于增强多关系图的学习. MRGCL有效地处理稀疏的数据,并通过使用层次关注和自适应图形增强来改进预测任务.

关键词:
相反的学习学习.可学习的图形增强.多关系图表学习多关系图表学习

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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
  • 图形表示学习学习学习图形表示学习

背景情况:

  • 多关系图学习将知识图实体和关系嵌入到低维表示中.
  • 与数据增强相反的学习,在解决多关系图形学习中的稀疏性方面显示出希望.

研究的目的:

  • 引入一种新的多关系图对比学习架构 (MRGCL),以改进多关系图的学习.
  • 通过解决数据稀疏性来提高多关系预测任务的性能.

主要方法:

  • 提出了一个多关系图层次关注网络 (MGHAN) 来识别实体重要性并提取局部图的依赖性.
  • 使用MGHAN的变体学习两个自适应图增强视图,适应各种多关系图数据集.
  • 设计一个子图的对比损失,从强烈连接的子图嵌入中产生正值.

主要成果:

  • 与最先进的方法相比,MRGCL架构表现出卓越的性能.
  • 在三个领域的多关系数据集上的实验验验证了拟议方法的有效性.
  • 该方法通过对比学习和数据增强成功处理了非常稀疏的数据.

结论:

  • 在多关系图表学习中,MRGCL提供了显著的进步,特别是对于稀疏的数据集.
  • 提出的层次关注和自适应增强策略是其有效性的关键.
  • 该架构在各种多关系图的学习任务中显示了广泛的适用性.