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

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
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...
1.9K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Associative Learning01:27

Associative Learning

572
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...
572
Observational Learning01:12

Observational Learning

311
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...
311
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

883
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...
883
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.1K
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...
1.1K

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

On Practical Robust Reinforcement Learning: Adjacent Uncertainty Set and Double-Agent Algorithm.

IEEE transactions on neural networks and learning systems·2024
Same author

Tighter Regret Analysis and Optimization of Online Federated Learning.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Online Multikernel Learning Method via Online Biconvex Optimization.

IEEE transactions on neural networks and learning systems·2023
Same author

Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Distributed Online Learning With Multiple Kernels.

IEEE transactions on neural networks and learning systems·2021
Same author

Active Learning With Multiple Kernels.

IEEE transactions on neural networks and learning systems·2021

相关实验视频

Updated: Sep 11, 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

681

FedLSC:提高与落后者和对手的联合学习中的沟通效率和稳定性.

Hyeong-Gun Joo, Songnam Hong, Dong-Joon Shin

    IEEE transactions on neural networks and learning systems
    |August 18, 2025
    PubMed
    概括

    本研究介绍了FedLSC,这是一个联合学习 (FL) 框架,可以在没有公开数据的情况下提高效率和稳定性. 联邦电信通信系统 (FedLSC) 显著降低了通信成本,使得FL在现实应用中变得更加实用.

    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) 面临着落后者,对手和高通讯成本等挑战.
    • 现有的FL方法往往需要公开数据,这限制了现实世界的适用性和弹性.

    研究的目的:

    • 提出FedLSC,一个新的FL框架,旨在提高稳定性和效率.
    • 通过消除在培训期间依赖公共数据来解决当前FL方法的局限性.

    主要方法:

    • 为了提高稳定性和效率,FedLSC使用层选择相关性 (LSC).
    • 关键的创新包括层选择 (LS) 以减少通信,基于LS的缩放标志-随机梯度下降 (SSS) 以进行本地更新,以及基于LSC的聚合.
    • 该SSS方案减轻了量子化损失和通信开销.

    主要成果:

    • 联邦电信通信系统 (FedLSC) 显著降低了通信成本,实现了最先进方法的0.01%.
    • 该框架保持了性能,同时大幅减少了通信需求.
    • 评估表明,在带宽受限制的FL场景中,性能和效率强.

    结论:

    • 对于现代联合学习应用程序,FedLSC提供了一种实用且有弹性的解决方案.

    更多相关视频

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    575

    相关实验视频

    Last Updated: Sep 11, 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

    681
    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    575
  • 该框架有效地提高了FL系统的效率和稳定性.
  • 在通信带宽有限的环境中,FedLSC特别有利.