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arXiv · 2410.19352

Interpreting Neural Networks through Mahalanobis Distance

Alan Oursland
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Abstract

This paper introduces a theoretical framework that connects neural network linear layers with the Mahalanobis distance, offering a new perspective on neural network interpretability. While previous studies have explored activation functions primarily for performance optimization, our work interprets these functions through statistical distance measures, a less explored area in neural network research. By establishing this connection, we provide a foundation for developing more interpretable neural network models, which is crucial for applications requiring transparency. Although this work is theoretical and does not include empirical data, the proposed distance-based interpretation has the potential to enhance model robustness, improve generalization, and provide more intuitive explanations of neural network decisions.

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Alan Oursland. 2024-10-25. Interpreting Neural Networks through Mahalanobis Distance. https://arxiv.org/abs/2410.19352

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