Towards machine learning models robust to adversarial examples and backdoor attacks
In the past decade, machine learning spectacularly succeeded on many challenging benchmarks. However, are our machine learning models ready to leave this lab setting and be safely deployed in high-stakes real-world applications? In this thesis, we take steps towards making this vision a reality by d...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/147387 |