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Presentamos mL-BFGS, un algoritmo basado en el impulso que mejora los métodos cuasi-Newton para el entrenamiento de redes neuronales profundas. Este método estabiliza la convergencia y acelera el entrenamiento para modelos distribuidos a gran escala.

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Área de la Ciencia:

  • Aprendizaje automático
  • Algoritmos de optimización
  • Las redes neuronales profundas

Sus antecedentes:

  • Los métodos cuasi-Newton, incluido el L-BFGS, enfrentan desafíos en el entrenamiento de redes neuronales profundas a gran escala debido a los costos computacionales y la inestabilidad en entornos estocásticos.
  • Las adaptaciones existentes de L-BFGS para el entrenamiento estocástico a menudo introducen gastos generales significativos, negando los beneficios de la convergencia.

Objetivo del estudio:

  • Proponer mL-BFGS, un algoritmo L-BFGS ligero basado en el impulso diseñado para una optimización eficiente de redes neuronales profundas distribuidas a gran escala.
  • Mejorar la estabilidad y reducir la carga computacional de los métodos cuasi-Newton en el aprendizaje profundo.

Principales métodos:

  • Desarrollado mL-BFGS, incorporando un esquema de impulso en la actualización L-BFGS para mitigar el ruido estocástico en las aproximaciones de Hesse.
  • Implementación de la aproximación de Hessian en mL-BFGS para distribuir los costos computacionales y de memoria entre los nodos para el entrenamiento a gran escala.
  • Proporcionó un análisis teórico de convergencia para mL-BFGS en escenarios de optimización estocástica.

Principales resultados:

  • mL-BFGS demostró convergencia estabilizada durante la optimización estocástica mediante la reducción del ruido en las aproximaciones de Hessian.
  • La aproximación de Hessian en bloques permitió un escalamiento eficiente de la computación y la memoria para el entrenamiento distribuido.
  • Los resultados empíricos en modelos neuronales de referencia mostraron una aceleración significativa en cuanto a iteración y reloj de pared en comparación con los métodos de referencia como SGD y Adam.

Conclusiones:

  • mL-BFGS ofrece un enfoque prometedor para aprovechar los métodos cuasi-Newton en el entrenamiento de redes neuronales profundas a gran escala.
  • El algoritmo propuesto equilibra efectivamente la eficiencia computacional con la estabilidad de convergencia, superando a los métodos existentes.