QVIP: An ILP-based formal verification approach for quantized neural networks
Deep learning has become a promising programming paradigm in software development, owing to its surprising performance in solving many challenging tasks. Deep neural networks (DNNs) are increasingly being deployed in practice, but are limited on resource-constrained devices owing to their demand for...
Saved in:
Main Authors: | ZHANG, Yedi, ZHAO, Zhe, CHEN, Guangke, SONG, Fu, ZHANG, Min, CHEN, Taolue, SUN, Jun |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2022
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/7281 https://ink.library.smu.edu.sg/context/sis_research/article/8284/viewcontent/ase22.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
ENHANCING THE EFFICACY OF NEURAL NETWORK ROBUSTNESS ANALYSIS
by: ZHONG YUYI
Published: (2023) -
QEBVerif: Quantization error bound verification of neural networks
by: ZHANG, Yedi, et al.
Published: (2023) -
Towards formal modeling and verification of cloud architectures: A case study on hadoop
by: Reddy, G.S., et al.
Published: (2014) -
Systematic and automatic verification of sensor networks
by: ZHENG MANCHUN
Published: (2013) -
Formalizing UML state machines for automated verification: A survey
by: ETIENE, Andre, et al.
Published: (2023)