Adversarial machine learning phases of matter
Abstract We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations: adding a tiny amount of carefully c...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-11-01
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| Series: | Quantum Frontiers |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44214-023-00043-z |