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相关概念视频

Associative Learning01:27

Associative Learning

243
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...
243
Multimachine Stability01:25

Multimachine Stability

103
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
103
Observational Learning01:12

Observational Learning

101
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...
101
Cognitive Learning01:21

Cognitive Learning

96
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...
96
Introduction to Learning01:18

Introduction to Learning

303
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...
303
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

88
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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相关实验视频

Updated: May 12, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.3K

联合学习与联合的服务器-客户端动力.

Boyuan Li1,2, Shaohui Zhang3,4, Qiuying Han5

  • 1School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China. ieboyuan@163.com.

Scientific reports
|May 5, 2025
PubMed
概括

联合学习面临数据异质性带来的挑战. 我们的新联合联合服务器-客户端动量 (FedJSCM) 算法通过使用梯度动量来提高模型准确性,以实现更稳定的训练.

关键词:
数据异质性 数据异质性分布式学习 分布式学习边缘计算 边缘计算联邦学习学习 (Federated Learning) 是一种学习方式.物联网的物联网,就是物联网.

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

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相关实验视频

Last Updated: May 12, 2025

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06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

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9.3K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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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:

  • 联合学习 (FL) 实现了分散的协作模式培训.
  • 当地数据异质性显著影响FL算法性能.
  • 解决数据异质性对于现实世界FL应用至关重要.

研究的目的:

  • 介绍联邦联合服务器-客户端势头 (FedJSCM),一个新的FL算法.
  • 缓解FL数据异质性的负面影响.
  • 提高FL算法的稳定性和性能.

主要方法:

  • FedJSCM在服务器和客户端之间传输梯度动量信息.
  • 使用动量调整客户端梯度下降和服务器模型融合.
  • 采用理论分析和广泛的实证研究.

主要成果:

  • 在各种任务中,FedJSCM表现出卓越的性能.
  • 显示了对不同程度的数据异质性的稳定性.
  • 与现有方法相比,模型准确度提高了1-3%.

结论:

  • 在联邦学习中,FedJSCM有效地解决了数据异质性.
  • 该算法提高了随机梯度下降 (SGD) 的稳定性.
  • 对于实际的FL部署,FedJSCM提供了一个有前途的解决方案.