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

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

252
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
252

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

Low-Temperature Triggered Shape Transformation of Liquid Metal Microdroplets.

ACS applied materials & interfaces·2020
Same author

Organohalogen compounds of emerging concern in Baltic Sea biota: Levels, biomagnification potential and comparisons with legacy contaminants.

Environment international·2020
Same author

Water-Soluble Anthraquinone Photocatalysts Enable Methanol-Driven Enzymatic Halogenation and Hydroxylation Reactions.

ACS catalysis·2020
Same author

Integrated sequencing and array comparative genomic hybridization in familial Parkinson disease.

Neurology. Genetics·2020
Same author

Treatment for tuberculosis of the subaxial cervical spine: a systematic review.

Archives of orthopaedic and trauma surgery·2020
Same author

A diagnostic model of idiopathic central precocious puberty based on transrectal pelvic ultrasound and basal gonadotropin levels.

The Journal of international medical research·2020
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
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

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

相关实验视频

Updated: Jun 25, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

为无通信的分布式深度学习提供光谱张量层.

Xiao-Yang Liu, Xiaodong Wang, Bo Yuan

    IEEE transactions on neural networks and learning systems
    |May 29, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了一种新的光谱张量网络,用于无通信的分布式深度学习. 这种方法增强了概括性,并降低了计算成本,使其成为联合学习中异质数据的理想选择.

    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

    4.2K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.7K

    相关实验视频

    Last Updated: Jun 25, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.7K
    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

    4.2K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.7K

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 分布式深度学习通常需要大量的通信开销.
    • 处理异质数据,特别是具有不同分辨率的数据,在联合学习中是一个挑战.

    研究的目的:

    • 为无通信分布式深度学习提出一种新的光谱张量层.
    • 实现对异质数据集的高效培训,而无需节点间通信.

    主要方法:

    • 以张量形式表示数据,并用张量产品取代矩阵产品.
    • 将数据集分割为光谱子数据集,使用线性转换进行并行处理.
    • 在子数据集上训练并行的网络分支组合在一起,形成最终的网络.

    主要成果:

    • 在多个数据集 (MNIST,CIFAR-10,ImageNet) 中实现了无通信的分布式学习.
    • 与传统网络相比,证明了减少内存和计算成本.
    • 用多分辨率异质数据展示了有效的学习.

    结论:

    • 谱张量网络为分布式深度学习提供了一个无通信,高效的解决方案.
    • 该方法在概括,存储减少和并行计算加快方面提供了好处.
    • 这是一个有前途的方法,用于具有不同数据分辨率的联合学习.