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相关概念视频

Associative Learning01:27

Associative Learning

597
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...
597
Introduction to Learning01:18

Introduction to Learning

545
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...
545
Purposive Learning01:22

Purposive Learning

209
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...
209
Observational Learning01:12

Observational Learning

319
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...
319
Cognitive Learning01:21

Cognitive Learning

589
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...
589
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

152
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...
152

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相关实验视频

Updated: Sep 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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Published on: December 6, 2024

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基于有条件的"散发到散发"计划的分散的个性化联合学习.

Qianyu Long, Qiyuan Wang, Christos Anagnostopoulos

    IEEE transactions on neural networks and learning systems
    |July 1, 2025
    PubMed
    概括

    分散式联合学习 (DFL) 的效率得到了DA-DPFL的提高,这是一种新的稀疏到稀疏的培训方法. 这种方法可以将能源成本降低多达5倍,同时在分散和个性化的学习场景中保持高测试准确性.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 分布式系统 分布式系统

    背景情况:

    • 分散的联合学习 (DFL) 提供了稳健性,并消除了中央协调,但造成了高的培训和通信成本.
    • 现有的DFL方法往往优先考虑通信效率,而不是培训效率和数据异质性挑战.

    研究的目的:

    • 引入DA-DPFL,这是一个新的稀疏到稀疏的DFL培训计划.
    • 解决DFL的培训效率和数据异质性问题.
    • 为了降低DFL的能源消耗,同时保持模型性能.

    主要方法:

    • DA-DPFL采用稀疏到稀疏的培训方案,以参数的子集开始.
    • 模型参数在训练过程中通过动态聚合逐渐减少.
    • 为分散和个性化的学习提供理论融合分析.

    主要成果:

    • 在测试准确性方面,DA-DPFL显著优于DFL基线.
    • 与DFL基线相比,能源成本降低了多达5倍.
    • 在分散和个性化的学习环境中证明了适用性.

    结论:

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  • 在不影响精度的情况下,DA-DPFL有效地降低了DFL的能耗.
  • 稀疏到稀疏的方法提高了培训效率,并处理了数据异质性.
  • DA-DPFL为节能和强大的分散式学习提供了一个可行的解决方案.