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相关概念视频

Associative Learning01:27

Associative Learning

239
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...
239
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

11.5K
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.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
11.5K
Observational Learning01:12

Observational Learning

98
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...
98
Introduction to Learning01:18

Introduction to Learning

302
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.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
302
Cognitive Learning01:21

Cognitive Learning

94
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.
Tolman introduced the idea that behavior is influenced by...
94
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

255
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
255

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相关实验视频

Updated: May 10, 2025

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
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阿里尔:对抗式图形对比式学习

Shengyu Feng1, Baoyu Jing2, Yada Zhu3

  • 1Carnegie Mellon University, USA.

ACM transactions on knowledge discovery from data
|April 23, 2025
PubMed
概括
此摘要是机器生成的。

逆向图形对比学习 (ArieL) 引入了对数据增强的逆向视图,改善了无监督图形表示学习. 这种方法产生了高质量的对比样本,在节点和图形分类任务中表现优于现有技术.

关键词:
进行对抗性培训.相反的学习学习学习.图表表示学习学习学习图表表示学习这是相互信息的互惠.

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科学领域:

  • 图形表示学习学习学习图形表示学习
  • 没有监督的学习学习.
  • 机器学习 机器学习

背景情况:

  • 对比式学习是无监督图表表示学习的关键,依赖于正负样本构造.
  • 现有的方法经常使用节点接近,而从计算机视觉中增强数据显示出有希望但对图形来说具有挑战性.
  • 为图形数据增强生成高质量的对比样本仍然是一个可以改进的开放领域.

研究的目的:

  • 提出一种简单而有效的方法,即对抗图对比学习 (ArieL),用于在图形表示学习中提取信息对比样本.
  • 通过引入对抗式图形视图来解决图形领域数据增强的挑战.
  • 为节点级和图形级对比学习概括拟议的方法.

主要方法:

  • 引入了对数据增强的对抗图形视图,以生成信息化的对比样本.
  • 开发了信息规范化,以提供稳定的培训,并为可扩展性进行子图采样.
  • 通过将图形视为超级节点,将节点级别的方法推广到图形级别的对比学习.

主要成果:

  • ArieL在现实数据集上的当前图形对比学习方法在节点级和图形级分类方面始终表现出色.
  • 证明了ArieL对抗敌对攻击的增强强性.
  • 在合理的限制范围内成功提取了高质量的对比样本.

结论:

  • 逆向图形对比学习 (ArieL) 为无监督图形表示学习提供了一种强大的新方法.
  • 该方法有效地解决了对比学习图形数据增强的局限性.
  • 阿里尔显示了改善图形分类任务和稳定性的巨大潜力.