The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning

• URL: http://arxiv.org/abs/2309.07072v2
• Date: Thu, 21 Nov 2024 14:10:03 GMT
• Title: The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning
• Authors: Alexander Bastounis, Alexander N. Gorban, Anders C. Hansen, Desmond J. Higham, Danil Prokhorov, Oliver Sutton, Ivan Y. Tyukin, Qinghua Zhou,
• Abstract summary: We consider classical distribution-agnostic framework and algorithms minimising empirical risks. We show that there is a large family of tasks for which computing and verifying ideal stable and accurate neural networks is extremely challenging.
• Score: 71.14237199051276
• License:
• Abstract: In this work, we assess the theoretical limitations of determining guaranteed stability and accuracy of neural networks in classification tasks. We consider classical distribution-agnostic framework and algorithms minimising empirical risks and potentially subjected to some weights regularisation. We show that there is a large family of tasks for which computing and verifying ideal stable and accurate neural networks in the above settings is extremely challenging, if at all possible, even when such ideal solutions exist within the given class of neural architectures.

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