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相关概念视频

Observational Learning01:12

Observational Learning

163
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...
163
Cognitive Learning01:21

Cognitive Learning

237
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...
237
Introduction to Learning01:18

Introduction to Learning

360
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...
360
Associative Learning01:27

Associative Learning

335
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...
335
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
Purposive Learning01:22

Purposive Learning

114
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...
114

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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自平衡的增量广泛学习系统,具有隐私保护.

Weiwen Zhang1, Ziyu Liu1, Yifeng Jiang1

  • 1School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China.

Neural networks : the official journal of the International Neural Network Society
|June 22, 2024
PubMed
概括

自平衡增量广泛学习系统 (SIBLS) 增强了多客户端学习中的隐私. 它实现了与现有方法可比的性能,同时提高了比联合学习的准确性和速度.

关键词:
广泛的学习系统.客户的选择 客户的选择增量学习是一种增量学习.保护隐私 保护隐私 保护隐私

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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:

  • 广泛的学习系统 (BLS) 提供高效的改造,而不需要再培训.
  • 现有的隐私解决方案,如多方安全广泛学习系统 (MSBLS),仅限于两个客户端.
  • 跨多个客户端的隐私保护增量学习仍然是一个未经探索的领域.

研究的目的:

  • 为维护隐私的多客户端增量学习提出一种新的自平衡增量广泛学习系统 (SIBLS).
  • 为了应对不同客户的数据样本大小变化的挑战.
  • 在多客户端广泛的学习环境中增强安全性.

主要方法:

  • 开发了一个客户选择策略,在增量更新期间平衡数据样本大小.
  • 引入了一个安全数据加密和特征映射的调解器.
  • 使用MNIST,时尚和NORB数据集实现和验证SIBLS.

主要成果:

  • SIBLS的表现与MSBLS的表现相当.
  • 在准确性和运行时间方面,SIBLS的表现优于联合学习.
  • 建议的客户选择和调解策略有效地提高了隐私和效率.

结论:

  • 在多客户端广义学习系统中,SIBLS为保护隐私的增量学习提供了有效的解决方案.
  • 该系统平衡数据分布,并确保安全性,优于传统的联合学习.
  • 这项工作推进了分布式系统的隐私保护机器学习领域.