2 papers
cs.LG2024
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning
Alexander Bastounis, Alexander N. Gorban, Anders C. Hansen +5
In this work, we assess the theoretical limitations of determining guaranteed stability and accuracy of neural networks in classification tasks. We consider classical distribution-…
cs.LG2024
How adversarial attacks can disrupt seemingly stable accurate classifiers
Oliver J. Sutton, Qinghua Zhou, Ivan Y. Tyukin +3
Adversarial attacks dramatically change the output of an otherwise accurate learning system using a seemingly inconsequential modification to a piece of input data. Paradoxically,…