Representaciones del aprendizaje reutilizando los gradientes de la retropropagacion

TL;DR

This paper introduces an algorithm leveraging backpropagation gradients to identify feature importance during training and visually represent the learning process, demonstrated on the Wisconsin cancer dataset.

Contribution

It presents a novel method to analyze and visualize the learning process of neural networks using gradient-based feature importance.

Findings

Gradients converge towards key features.

Effective visualization of learning stages.

Identified important variables in cancer dataset.

Abstract

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.