An Interpretable Neural Network Model Through Piecewise Linear Approximation
Abstract
Most existing interpretable methods explain a black-box model in a post-hoc manner, which uses simpler models or data analysis techniques to interpret the predictions after the model is learned. However, they (a) may derive contradictory explanations on the same predictions given different methods and data samples, and (b) focus on using simpler models to provide higher descriptive accuracy at the sacrifice of prediction accuracy. To address...
Paper Details
Title
An Interpretable Neural Network Model Through Piecewise Linear Approximation
Published Date
Jan 20, 2020
Journal
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