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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

16.6K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
16.6K
Vector Operations01:20

Vector Operations

1.9K
Vectors are physical quantities that have both magnitude and direction. The vector operations include addition, subtraction, and scalar multiplication.
A vector multiplied by a scalar value is called scalar multiplication. The result obtained is a new vector with a different magnitude. If the scalar is positive, the direction of the vector remains the same, but if it is negative, the direction of the vector is reversed. For example, the product of the mass and velocity yields the momentum.
1.9K
Vectors01:30

Vectors

224
Vectors are mathematical entities characterized by both magnitude and direction. Unlike scalars, which are defined solely by magnitude, vectors represent quantities like displacement, velocity, and force, where direction is essential. Vectors are graphically represented as directed line segments, extending from an initial point to a terminal point, denoted with bold letters or arrows placed above the symbol. Two vectors are deemed equal if they share identical magnitudes and directions,...
224
Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

26.6K
Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
26.6K
Cartesian Vector Notation01:28

Cartesian Vector Notation

1.3K
Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...
1.3K
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

447
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
447

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

Multiclass Linear Perceptrons With Multiplicative Margins.

Neural computation·2026
Same author

Editorial: Machine learning for cybersecurity.

Frontiers in artificial intelligence·2025
Same author

Preferences for Telephone Cancer Information and Support in People with Cancer and Carers: Attribute and Level Selection for a Discrete Choice Experiment.

The patient·2025
Same author

The Challenges of Gender Diversity in Boards of Directors: An Australian Study with Global Implications.

Global challenges (Hoboken, NJ)·2025
Same author

ChatGPT and generative AI in urology and surgery-A narrative review.

BJUI compass·2024
Same author

Artificial Intelligence-Based Co-Facilitator (AICF) for Detecting and Monitoring Group Cohesion Outcomes in Web-Based Cancer Support Groups: Single-Arm Trial Study.

JMIR cancer·2024
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

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

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

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

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

图形向量函数架构 图形向量函数架构

Sachin Kahawala1, Daswin De Silva1, Evgeny Osipov2

  • 1Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia.

Neural networks : the official journal of the International Neural Network Society
|December 22, 2025
PubMed
概括
此摘要是机器生成的。

图形向量函数架构 (GVFA) 为图形神经网络 (GNN) 提供了一个新的,高效的替代方案. 这种零射击方法提供了一般的图形表示,没有特定任务的学习,大大减少了计算成本和培训时间.

关键词:
图形神经网络是一个神经网络.图形表示图形表示.超维的计算超维的计算.矢量函数的架构是矢量函数的架构.零射击图表学习的零射击图.

更多相关视频

High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

33.7K
Generating Recombinant Avian Herpesvirus Vectors with CRISPR/Cas9 Gene Editing
12:21

Generating Recombinant Avian Herpesvirus Vectors with CRISPR/Cas9 Gene Editing

Published on: January 7, 2019

14.0K

相关实验视频

Last Updated: Jan 8, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K
High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

33.7K
Generating Recombinant Avian Herpesvirus Vectors with CRISPR/Cas9 Gene Editing
12:21

Generating Recombinant Avian Herpesvirus Vectors with CRISPR/Cas9 Gene Editing

Published on: January 7, 2019

14.0K

科学领域:

  • 机器学习 机器学习
  • 图形表示学习学习学习图形表示学习
  • 超维计算的超维计算

背景情况:

  • 图形神经网络 (GNN) 在关系数据中很普遍,但在计算上昂贵且低效.
  • 现有的方法通常需要特定任务的学习,增加计算负载.

研究的目的:

  • 介绍图形向量函数架构 (GVFA) 作为学习图形表示的新,高效的替代方案.
  • 为图形和节点表示开发一种通用,零射击的方法,绕过传统的GNN学习.

主要方法:

  • 利用超维计算 (HDC) 的原理来开发GVFA.
  • 实现了GVFA作为一种一般的,未经训练的方法来创建图形和节点表示.
  • 评估了GVFA在各种配置中的表达力和概括能力.

主要成果:

  • 在图形和节点分类任务中,GVFA表现强.
  • 在准确性方面,GVFA在基准数据集上表现优于几个经典的GNN.
  • 与基于学习的GNN相比,GVFA实现了培训时间的大幅减少.

结论:

  • GVFA提供了一种有效和计算效率高的方法来学习图形表示.
  • GVFA的零射击,未经训练的性质比传统的GNN提供了显著的优势.
  • GVFA为高效和可泛化的图表表示学习提供了一个有希望的方向.