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Characterizing and Understanding the Behavior of Quantized Models for
  Reliable Deployment

Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment

8 April 2022
Qiang Hu
Yuejun Guo
Maxime Cordy
Xiaofei Xie
Wei Ma
Mike Papadakis
Yves Le Traon
    MQ
ArXivPDFHTML

Papers citing "Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment"

4 / 4 papers shown
Title
Post-Training Sparsity-Aware Quantization
Post-Training Sparsity-Aware Quantization
Gil Shomron
F. Gabbay
Samer Kurzum
U. Weiser
MQ
31
33
0
23 May 2021
Distribution-Aware Testing of Neural Networks Using Generative Models
Distribution-Aware Testing of Neural Networks Using Generative Models
Swaroopa Dola
Matthew B. Dwyer
M. Soffa
32
52
0
26 Feb 2021
An Empirical Study on Deployment Faults of Deep Learning Based Mobile
  Applications
An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications
Zhenpeng Chen
Huihan Yao
Yiling Lou
Yanbin Cao
Yuanqiang Liu
Haoyu Wang
Xuanzhe Liu
40
79
0
13 Jan 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
279
9,136
0
06 Jun 2015
1