ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs
Mixed precision quantization has become an important technique for optimizing the execution of deep neural networks (DNNs). Certified robustness, which provides provable guarantees about a model’s ability to withstand different adversarial perturbations, has rarely been addressed in quantization due to the unacceptably...
Yuchen Yang, Yifan Zhao, Shubham Ugare et al.
· Proceedings of the ACM on so... · 1 citation