Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Randomized Experiments01:13

Randomized Experiments

8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.8K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.1K
Introduction to Learning01:18

Introduction to Learning

917
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...
917
Associative Learning01:27

Associative Learning

1.2K
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...
1.2K
Cognitive Learning01:21

Cognitive Learning

991
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...
991
Observational Learning01:12

Observational Learning

807
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...
807

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Learning from sanctioned government suppliers: a machine learning and network science approach to detecting fraud and corruption in Mexico.

Scientific reports·2026
Same author

Human mobility in the metaverse mirrors patterns in the physical world.

Scientific reports·2026
Same author

Social inequalities in vaccine coverage and their effects on epidemic spreading.

PLoS computational biology·2025
Same author

Logarithmic kinetics and bundling in random packings of elongated 3D physical links.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

When dialects collide: how socioeconomic mixing affects language use.

EPJ data science·2025
Same author

Milgram's experiment in the knowledge space: individual navigation strategies.

EPJ data science·2025
Same journal

Changes in patient-sharing patterns after oncologist departures in rural and urban settings: a Medicare cohort study.

Applied network science·2026
Same journal

Tunable network properties with Hamill and Gilbert's Social Circles generator.

Applied network science·2025
Same journal

The association of prescriber prominence in a shared-patient physician network with their patients receipt of and transitions between risky drug combinations.

Applied network science·2025
Same journal

Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation.

Applied network science·2025
Same journal

Navigation on temporal networks.

Applied network science·2025
Same journal

Leading by the nodes: a survey of film industry network analysis and datasets.

Applied network science·2024
查看所有相关文章

相关实验视频

Updated: Jun 19, 2026

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

在分散的联合学习中,初始化和网络效应.

Arash Badie-Modiri1,2, Chiara Boldrini3, Lorenzo Valerio3

  • 1Department of Network and Data Science, Central European University, 1100 Vienna, Austria.

Applied network science
|November 3, 2025
PubMed
概括
此摘要是机器生成的。

完全去中心化的联合学习列车在本地数据上协作模式,增强隐私. 一个基于网络拓学的新初始化策略显著提高了人工神经网络的训练效率.

关键词:
复杂的网络是一个复杂的网络.联合学习是联合学习.八协议 八协议 八协议随机走路可以随机走.

更多相关视频

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education
04:15

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education

Published on: February 23, 2024

相关实验视频

Last Updated: Jun 19, 2026

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education
04:15

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education

Published on: February 23, 2024

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 网络科学 网络科学

背景情况:

  • 分散的联合学习 (DFL) 允许在没有中央协调的情况下进行协作模式培训,从而保护数据隐私.
  • DFL的性能对网络拓和初始模型条件敏感.
  • 现有的DFL方法往往缺乏高效,无协调的初始化策略.

研究的目的:

  • 为DFL提出一个新的初始化策略,利用网络拓.
  • 提高分散的人工神经网络的培训效率和可扩展性.
  • 调查网络结构对DFL动态的影响.

主要方法:

  • 开发了基于自身向量中心分布的人工神经网络的无协调初始化策略.
  • 分析了网络拓对DFL性能的影响.
  • 在拟议的初始化下研究了缩放行为和参数选择.

主要成果:

  • 拟议的初始化策略显著提高了去中心化联合学习的效率.
  • 证明了网络拓,初始化和学习动态之间的明显联系.
  • 为拟议的初始化战略确定了最佳的环境参数.

结论:

  • 网络结构和初始化对于高效的DFL至关重要.
  • 拟议的方法为分散的AI培训提供了可扩展和有效的方法.
  • 这项研究为设计强大的DFL系统提供了基础的见解.