Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning
Machine-learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signaling, like in early immune recognition. We draw a formal analogy between neural networks used in machine...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2019-07-01
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| Series: | Physical Review X |
| Online Access: | http://doi.org/10.1103/PhysRevX.9.031012 |