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Poster
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Matthew Mirman · Timon Gehr · Martin Vechev
We introduce a scalable method for training robust neural networks based on abstract interpretation. We present several abstract transformers which balance efficiency with precision and show these can be used to train large neural networks that are certifiably robust to adversarial perturbations.
Author Information
Matthew Mirman (ETH Zürich)
Timon Gehr (ETH Zurich)
Martin Vechev (ETH Zurich)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Differentiable Abstract Interpretation for Provably Robust Neural Networks »
Wed. Jul 11th 03:00 -- 03:20 PM Room A7
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