Oral
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Matthew Mirman · Timon Gehr · Martin Vechev
Abstract:
We introduce a scalable method for training neural networks based on abstract interpretation. We show how to successfully apply an approximate end-to-end differentiable abstract interpreter to train large networks that are (i) certifiably more robust to adversarial perturbations, and (ii) have improved accuracy.
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