7 citations · 13 across the 9 of their papers we have counts for
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cs.LG2023★ 1 cited
Towards Efficient Verification of Quantized Neural Networks
Pei Huang, Haoze Wu, Yuting Yang +4
Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory. In t…
cs.LG2022
QEBVerif: Quantization Error Bound Verification of Neural Networks
Yedi Zhang, Fu Song, Jun Sun
To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge devices, quantization is widely regarded as one promising technique. It reduces the resourc…
cs.LG2021★ 1 cited
BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks
Yedi Zhang, Zhe Zhao, Guangke Chen +2
Verifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications. In this paper, we st…