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相关概念视频

Observational Learning01:12

Observational Learning

111
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...
111
Associative Learning01:27

Associative Learning

270
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...
270
Introduction to Learning01:18

Introduction to Learning

318
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...
318
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

2.5K
2.5K
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.
Tolman introduced the idea that behavior is influenced by...
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相关实验视频

Updated: May 21, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

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ERMAV:高效和强大的图形通过多对手观点对比学习培训培训

Wen Li, Wing W Y Ng, Hengyou Wang

    IEEE transactions on cybernetics
    |March 20, 2025
    PubMed
    概括

    本研究介绍了ERMAV,一个高效和强大的图形对比学习 (GCL) 框架. 通过使用多对手视图,ERMAV增强了GCL对对手攻击的弹性,提高了攻击图的性能.

    科学领域:

    • 图形表示学习学习学习图形表示学习
    • 机器学习安全 机器学习安全
    • 人工智能的人工智能

    背景情况:

    • 图形对比学习 (GCL) 对于图形表示学习至关重要.
    • 现有的GCL方法容易受到对抗性攻击.
    • 目前强大的GCL方法在计算上昂贵,缺乏可扩展性.

    研究的目的:

    • 提出一个高效和强大的GCL框架来抵御对抗性攻击.
    • 解决现有的强大的GCL方法的低效率和可扩展性问题.

    主要方法:

    • 引入了ERMAV (通过多对手观点培训提供高效和强大的GCL).
    • 通过攻击节点属性和子图上的潜在表示来生成对抗性视图.
    • 采用高效的攻击方法来产生动态的敌对干扰.

    主要成果:

    • 在原始图表上,ERMAV的性能优于最先进的GCL方法.
    • 与攻击图表上的现有方法相比,ERMAV表现出优越的稳定性.
    • 在七个现实世界数据集上进行了广泛的实验,验证了框架的有效性.

    结论:

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  • 对于强大的 GCL,ERMAV 提供了一种高效且可扩展的解决方案.
  • 拟议的多对手观点培训增强了GCL的弹性.
  • 对于需要强大的图形表示的现实世界应用程序,ERMAV显示出巨大的潜力.