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The mathematics of adversarial attacks in AI – why deep learning is unstable despite the existence of stable neural networks.

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Title: The mathematics of adversarial attacks in AI – why deep learning is unstable despite the existence of stable neural networks.
Authors: Bastounis, Alexander1, Hansen, Anders2, ach70@cam.ac.uk, Vlačić, Verner3
Source: European Journal of Applied Mathematics; Feb2026, Vol. 37 Issue 1, p259-289, 31p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 190671936
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PubType: Academic Journal
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  Data: The mathematics of adversarial attacks in AI – why deep learning is unstable despite the existence of stable neural networks.
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Applied+Mathematics%22">European Journal of Applied Mathematics</searchLink>; Feb2026, Vol. 37 Issue 1, p259-289, 31p
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        Value: 10.1017/S0956792525100193
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      – Code: eng
        Text: English
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        PageCount: 31
        StartPage: 259
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      – TitleFull: The mathematics of adversarial attacks in AI – why deep learning is unstable despite the existence of stable neural networks.
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              Text: Feb2026
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              Y: 2026
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