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

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

570
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...
570
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

407
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
407
Associative Learning01:27

Associative Learning

246
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...
246
Random and Systematic Errors01:20

Random and Systematic Errors

10.7K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
10.7K
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
Aggregates Classification01:29

Aggregates Classification

290
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
290

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

A retrospective study on the application of unilateral epidural anesthesia in older patients with hip fracture.

BMC geriatrics·2026
Same author

mRNA-LNP vaccine providing antigen and co-stimulation in the tumor microenvironment enhances CAR T cell function (CART-Vac).

Molecular therapy. Oncology·2026
Same author

Climate change exacerbates disparities of energy resilience in New York City.

Nature communications·2026
Same author

Improving human motion generation based on a head-mounted display and its controllers via noise-augmented motion data and the recurrent inference model.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Quantitative detection modeling of lysine and histidine in animal feed based on terahertz metamaterial sensing technology.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Integrated framework to study genomic surveillance of selective sweeps in multivariants dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: May 13, 2025

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

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

Published on: December 6, 2024

463

在通信错误下路线和聚合分散的联合学习.

Weicai Li, Tiejun Lv, Wei Ni

    IEEE transactions on neural networks and learning systems
    |April 15, 2025
    PubMed
    概括
    此摘要是机器生成的。

    路由和聚合 (R&A) 分散的联合学习 (D-FL) 通过通过既定的路径有效地路由数据来提高模型训练的准确性. 这种方法优于传统的八协议,特别是在没有参与节点的网络中.

    更多相关视频

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.4K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    457

    相关实验视频

    Last Updated: May 13, 2025

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

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

    Published on: December 6, 2024

    463
    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.4K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    457

    科学领域:

    • 机器学习 机器学习
    • 分布式系统 分布式系统
    • 网络通讯 网络通讯 网络通讯

    背景情况:

    • 分散的联合学习 (D-FL) 提供可扩展的本地模型聚合.
    • 现有的D-FL方法经常使用低效的八协议,特别是当网络节点不是所有D-FL客户端时.

    研究的目的:

    • 引入一个新的D-FL战略,路线和聚合 (R&A) D-FL.
    • 分析路由和通信错误对R&A D-FL融合的影响.
    • 与现有方法相比,证明R&A D-FL的有效性.

    主要方法:

    • 开发了R&A D-FL,它使用已确定的路线进行模型交换,而不是洪水.
    • 包含聚合系数的自适应规范化,以处理通信错误.
    • 分析了基于端到端数据包错误率 (PERs) 的收性质.

    主要成果:

    • 在一个由10个客户组成的网络中,R&A D-FL在训练准确度上比基于洪水的D-FL提高了35%.
    • 当使用具有最小端到端数据包错误率的路线时,收是最佳的.
    • 随着路由节点的增加,R&A D-FL在通信错误下的准确性接近理想的集中式联合学习 (C-FL).

    结论:

    • R&A D-FL为分散的联合学习提供了一种更有效和更强大的方法.
    • 该方法显示了D-FL和网络协议之间的显著协同作用.
    • 在复杂的网络环境中,R&A D-FL有效地减轻了通信错误,提高了训练准确度.