Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Linearization and Approximation01:26

Linearization and Approximation

85
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
85
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

396
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
396
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

773
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
773
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

379
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
379
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

111
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
111
Spherical Coordinates01:23

Spherical Coordinates

16.3K
Spherical coordinate systems are preferred over Cartesian, polar, or cylindrical coordinates for systems with spherical symmetry. For example, to describe the surface of a sphere, Cartesian coordinates require all three coordinates. On the other hand, the spherical coordinate system requires only one parameter: the sphere's radius. As a result, the complicated mathematical calculations become simple. Spherical coordinates are used in science and engineering applications like electric and...
16.3K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Three-Dimensional Radiographic and Morphological Features of Ameloblastoma and Odontogenic Keratocyst: A Comparative Cone Beam Computed Tomography Study Using Automatic Segmentation.

International dental journal·2026
Same author

β-Caryophyllene Competes With NINJA/IAAs for TOPLESS Family Corepressors to Regulate Plant Physiological Activities.

Plant, cell & environment·2026
Same author

Characterization of a novel phage BUCT800 against Acinetobacter baumannii and its biofilm removal efficiency.

Archives of virology·2026
Same author

Characterization and complete genomic sequence of a novel phage BUCT805 infecting <i>Serratia marcescens</i> and its anti-biofilm activities.

Microbiology spectrum·2026
Same author

MambaFPN: A SSM-based feature pyramid network for object detection.

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

Generative AI-based low-dose digital subtraction angiography for intra-operative radiation dose reduction: a randomized controlled trial.

Nature medicine·2026
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: Feb 20, 2026

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

1.2K

Interpolación lineal esférica generalizada y grupal para la compresión de contexto a nivel de token

Jinhao Tian1, Zuchao Li2, Meng-Jia Shen3

  • 1School of Artificial Intelligence, Wuhan University, Wuhan, 430072, China; National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, 430072, China.

Neural networks : the official journal of the International Neural Network Society
|February 18, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Desarrollamos GSlerp-CC, utilizando interpolación lineal esférica (Slerp) para reducir las demandas computacionales en modelos de lenguaje. Este método comprime las longitudes de secuencia tanto para arquitecturas de solo codificador como de solo decodificador, mejorando la eficiencia.

Palabras clave:
compresión de caché KVmodelos de lenguaje grandes

Videos de Experimentos Relacionados

Last Updated: Feb 20, 2026

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

1.2K

Área de la Ciencia:

  • Procesamiento del Lenguaje Natural
  • Inteligencia Artificial
  • Aprendizaje Automático

Sus antecedentes:

  • Los modelos de lenguaje modernos ofrecen capacidades avanzadas pero requieren importantes recursos computacionales.
  • Los métodos existentes para optimizar modelos de lenguaje a menudo enfrentan limitaciones en la eficiencia de los recursos.

Objetivo del estudio:

  • Introducir GSlerp-CC, un enfoque novedoso para reducir la longitud de la secuencia en los cálculos de atención.
  • Mejorar la eficiencia de los recursos de las arquitecturas de modelos de lenguaje de solo codificador y de solo decodificador.

Principales métodos:

  • GSlerp-CC emplea dos técnicas basadas en interpolación lineal esférica (Slerp) para la compresión de contexto.
  • La Slerp generalizada fusiona el contexto explicativo del prompt en un token especial para modelos de solo codificador.
  • La Slerp grupal comprime la información de la caché de Clave/Valor en modelos de solo decodificador.

Principales resultados:

  • Experimentos exhaustivos demuestran la efectividad de GSlerp-CC en varios puntos de referencia.
  • El método reduce con éxito la longitud de la secuencia en los cálculos de atención.
  • Validado en la Evaluación de Interfaz Unificada (UIE), Comprensión del Lenguaje Natural (NLU) y Tareas de Texto Largo.

Conclusiones:

  • GSlerp-CC ofrece una solución eficaz para reducir los requisitos de recursos computacionales en modelos de lenguaje.
  • Los métodos propuestos basados en Slerp proporcionan importantes ganancias de eficiencia para diferentes arquitecturas de modelos.
  • Este trabajo avanza en el desarrollo de modelos de lenguaje más eficientes en recursos y escalables.