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相关概念视频

Associative Learning01:27

Associative Learning

404
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...
404
Observational Learning01:12

Observational Learning

182
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...
182
Cognitive Learning01:21

Cognitive Learning

246
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...
246
Introduction to Learning01:18

Introduction to Learning

441
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...
441
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.7K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.7K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
699

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联邦噪音客户端学习学习

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    此摘要是机器生成的。

    联合学习 (FL) 与杂的客户端作斗争,影响模型性能. 我们的Fed-NCL框架识别和减轻这些杂的客户,以获得更强大,更准确的协作模式培训.

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    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 数据 隐私 数据 隐私 数据

    背景情况:

    • 联合学习 (FL) 能够实现协作模式培训,同时通过分散的数据保护数据隐私.
    • 标准FL方法易受噪音客户端及其数据造成的性能下降的影响.

    研究的目的:

    • 调查噪音客户端对联合学习模型融合和性能的影响.
    • 提出一个新的框架,联邦杂客户端学习 (Fed-NCL),用于与杂客户端进行强大的联合学习.

    主要方法:

    • 量化了杂客户端对不同模型层学习表示的负面影响.
    • 开发了Fed-NCL,通过估计数据质量和模型分歧来识别杂的客户.
    • 实施了强大的分层聚合和标签校正,以解决数据异质性和改善概括性.

    主要成果:

    • 证实,杂的客户显著损害了FL的全球模式融合和业绩.
    • 观察到,与早期的层相比,杂的客户端在更深层中引入了更大的偏差.
    • 证明Fed-NCL在有噪音的客户面前提高了最先进的FL系统的性能.

    结论:

    • 噪音客户端对联合学习构成了关键挑战,特别影响更深层模型层.
    • 联储-NCL有效地识别和减轻噪音客户的负面影响,从而提高了模型的稳定性.
    • 拟议的框架为构建更可靠的联合学习系统提供了一种实际解决方案.