ArticleScientific reports2024
Exploring the uncertainty principle in neural networks through binary classification.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Neural networks are reported to be vulnerable under minor and imperceptible attacks. The underlying mechanism and quantitative measure of the vulnerability still remains to be revealed. In this study, we explore the intrinsic trade-off between accuracy and robustness in neural networks, framed through the lens of the "uncertainty principle". By examining the fundamental limitations imposed by this principle, we reveal how neural networks inherently balance precision in feature extraction with susceptibility to adversarial perturbations. Our analysis highlights that as neural networks achieve higher accuracy, their vulnerability to adversarial attacks increases, a phenomenon rooted in the uncertainty relation. By using the mathematics from quantum mechanics, we offer a theoretical foundation and analytical method for understanding the vulnerabilities of deep learning models.
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