ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1909.06727
  4. Cited By
An Empirical Study towards Characterizing Deep Learning Development and
  Deployment across Different Frameworks and Platforms

An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms

15 September 2019
Qianyu Guo
Sen Chen
Xiaofei Xie
Lei Ma
Q. Hu
Hongtao Liu
Yang Liu
Jianjun Zhao
Xiaohong Li
ArXivPDFHTML

Papers citing "An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms"

10 / 10 papers shown
Title
Studying the Impact of TensorFlow and PyTorch Bindings on Machine
  Learning Software Quality
Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality
Hao Li
Gopi Krishnan Rajbahadur
C. Bezemer
34
5
0
07 Jul 2024
Quality at the Tail of Machine Learning Inference
Quality at the Tail of Machine Learning Inference
Zhengxin Yang
Wanling Gao
Chunjie Luo
Lei Wang
Fei Tang
Xu Wen
Jianfeng Zhan
28
1
0
25 Dec 2022
Towards Training Reproducible Deep Learning Models
Towards Training Reproducible Deep Learning Models
Boyuan Chen
Mingzhi Wen
Yong Shi
Dayi Lin
Gopi Krishnan Rajbahadur
Zhen Ming
Z. Jiang
SyDa
15
37
0
04 Feb 2022
Security for Machine Learning-based Software Systems: a survey of
  threats, practices and challenges
Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges
Huaming Chen
Muhammad Ali Babar
AAML
29
21
0
12 Jan 2022
Understanding Performance Problems in Deep Learning Systems
Understanding Performance Problems in Deep Learning Systems
Junming Cao
Bihuan Chen
Chao Sun
Longjie Hu
Shuai Wu
Xin Peng
30
26
0
03 Dec 2021
Smart at what cost? Characterising Mobile Deep Neural Networks in the
  wild
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
Mario Almeida
Stefanos Laskaridis
Abhinav Mehrotra
L. Dudziak
Ilias Leontiadis
Nicholas D. Lane
HAI
109
44
0
28 Sep 2021
Which Design Decisions in AI-enabled Mobile Applications Contribute to
  Greener AI?
Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?
Roger Creus Castanyer
Silverio Martínez-Fernández
Xavier Franch
38
11
0
28 Sep 2021
Integration of Convolutional Neural Networks in Mobile Applications
Integration of Convolutional Neural Networks in Mobile Applications
Roger Creus Castanyer
Silverio Martínez-Fernández
Xavier Franch
13
12
0
11 Mar 2021
A Performance-Sensitive Malware Detection System Using Deep Learning on
  Mobile Devices
A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices
Ruitao Feng
Sen Chen
Xiaofei Xie
Guozhu Meng
Shang-Wei Lin
Yang Liu
28
102
0
11 May 2020
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
226
1,835
0
03 Feb 2017
1