Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

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

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
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
Ver todos los artículos relacionados
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Video Experimental Relacionado

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

Arquitectura de función vectorial de grafos

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
Resumen
Este resumen es generado por máquina.

La Arquitectura de Función Vectorial de Grafos (GVFA) ofrece una alternativa novedosa y eficiente a las Redes Neuronales de Grafos (GNN). Este enfoque de tiro cero proporciona representaciones generales de grafos sin aprendizaje específico de tareas, lo que reduce significativamente los costos computacionales y el tiempo de entrenamiento.

Palabras clave:
redes neuronales de grafosrepresentación de grafoscomputación de alta dimensionalidadarquitectura de función vectorialaprendizaje de grafos de tiro cero

Más Videos Relacionados

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

Videos de Experimentos Relacionados

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

Área de la Ciencia:

  • Aprendizaje automático; Aprendizaje de representaciones de grafos; Computación de alta dimensionalidad

Sus antecedentes:

  • Las Redes Neuronales de Grafos (GNN) son prevalentes para datos relacionales, pero son computacionalmente costosas e ineficientes.; Los métodos existentes a menudo requieren aprendizaje específico de tareas, lo que aumenta la carga computacional.

Objetivo del estudio:

  • Presentar la Arquitectura de Función Vectorial de Grafos (GVFA) como una alternativa novedosa y eficiente para aprender representaciones de grafos.; Desarrollar un enfoque general de tiro cero para representaciones de grafos y nodos que eluda el aprendizaje tradicional de GNN.

Principales métodos:

  • Se utilizaron principios de computación de alta dimensionalidad (HDC) para desarrollar GVFA.; Se implementó GVFA como un enfoque general y no entrenado para crear representaciones de grafos y nodos.; Se evaluaron la expresividad y las capacidades de generalización de GVFA en varias configuraciones.

Principales resultados:

  • GVFA demostró un fuerte rendimiento en tareas de clasificación de grafos y nodos.; GVFA superó a varias GNN clásicas en conjuntos de datos de referencia en términos de precisión.; GVFA logró reducciones sustanciales en el tiempo de entrenamiento en comparación con las GNN basadas en aprendizaje.

Conclusiones:

  • GVFA proporciona un método eficaz y computacionalmente eficiente para el aprendizaje de representaciones de grafos.; La naturaleza de tiro cero y no entrenada de GVFA ofrece ventajas significativas sobre las GNN tradicionales.; GVFA presenta una dirección prometedora para el aprendizaje de representaciones de grafos eficiente y generalizable.