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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Video Experimental Relacionado

Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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DA-PFL: Agregación dinámica de afinidad en el aprendizaje federado personalizado bajo desequilibrio de clase

Xu Yang, Jiyuan Feng, Yongxin Tong

    IEEE transactions on neural networks and learning systems
    |September 3, 2025
    PubMed
    Resumen

    Este estudio introduce un modelo de aprendizaje federado personalizado basado en la afinidad dinámica (PFL) para abordar el desequilibrio de clase. El nuevo enfoque mejora la precisión del modelo para clientes individuales en escenarios de aprendizaje federados.

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    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

    Área de la Ciencia:

    • Inteligencia artificial
    • Aprendizaje automático
    • Sistemas distribuidos

    Sus antecedentes:

    • El aprendizaje federado personalizado (PFL) tiene como objetivo crear modelos personalizados para cada cliente.
    • Los métodos PFL actuales a menudo agregan clientes con distribuciones de datos similares.
    • Esta agregación basada en la similitud puede empeorar el problema del desequilibrio de clase en los conjuntos de datos.

    Objetivo del estudio:

    • Proponer un nuevo modelo dinámico basado en la afinidad (DA-PFL).
    • Para aliviar el problema de desequilibrio de clase inherente al aprendizaje federado.
    • Mejorar el rendimiento de los modelos de aprendizaje personalizado.

    Principales métodos:

    • Desarrolló una métrica de afinidad complementaria para guiar la agregación de clientes.
    • Implementó una estrategia de agregación dinámica ajustando la selección de clientes en cada ronda.
    • Evaluado el modelo DA-PFL en cuatro conjuntos de datos del mundo real.

    Principales resultados:

    • El modelo DA-PFL mejoró significativamente la precisión del cliente.
    • Redujeron efectivamente el impacto negativo del desequilibrio de clase durante el aprendizaje federado.
    • Superó a los métodos de comparación más avanzados existentes.

    Conclusiones:

    • El modelo DA-PFL propuesto ofrece una solución eficaz para el desequilibrio de clase en PFL.
    • La agregación dinámica basada en la afinidad mejora el rendimiento del modelo personalizado.
    • DA-PFL demuestra una precisión superior en diversos conjuntos de datos.