Related papers: Harmonic Machine Learning Models are Robust

Harmonic Machine Learning Models are Robust

URL: http://arxiv.org/abs/2404.18825v1
Date: Mon, 29 Apr 2024 16:07:36 GMT
Title: Harmonic Machine Learning Models are Robust
Authors: Nicholas S. Kersting, Yi Li, Aman Mohanty, Oyindamola Obisesan, Raphael Okochu,
Abstract summary: We introduce Harmonic Robustness, a powerful and intuitive method to test the robustness of any machine-learning model. It is based on functional deviation from the harmonic mean value property, indicating instability and lack of explainability.
Score: 3.263224198355111
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We introduce Harmonic Robustness, a powerful and intuitive method to test the robustness of any machine-learning model either during training or in black-box real-time inference monitoring without ground-truth labels. It is based on functional deviation from the harmonic mean value property, indicating instability and lack of explainability. We show implementation examples in low-dimensional trees and feedforward NNs, where the method reliably identifies overfitting, as well as in more complex high-dimensional models such as ResNet-50 and Vision Transformer where it efficiently measures adversarial vulnerability across image classes.

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