Representaciones del aprendizaje reutilizando los gradientes de la retropropagacion
This work offers an incremental method for understanding feature importance during neural network training, potentially benefiting researchers and practitioners in machine learning.
This paper proposes an algorithm that reuses backpropagation gradients to determine feature importance at different training stages. Experiments on the Wisconsin cancer dataset showed that these 'learning gradients' converge towards the most important features.
This work proposes an algorithm for taking advantage of backpropagation gradients to determine feature importance at different stages of training. Additionally, we propose a way to represent the learning process qualitatively. Experiments were performed over the Wisconsin cancer dataset provided by sklearn, and results showed an interesting convergence of the so called "learning gradients" towards the most important features. --- Este trabajo propone el algoritmo de gradientes de aprendizaje para encontrar significado en las entradas de una red neuronal. Ademas, se propone una manera de evaluarlas por orden de importancia y representar el proceso de aprendizaje a traves de las etapas de entrenamiento. Los resultados obtenidos utilizan como referencia el conjunto de datos acerca de tumores malignos y benignos en Wisconsin. Esta referencia sirvio para detectar un patron en las variables mas importantes del modelo gracias, asi como su evolucion temporal.