AI RESEARCH
ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs
arXiv CS.LG
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ArXi:2410.24214v3 Announce Type: replace 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 high cost of certifying robustness. This paper