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An Empirical Study of Challenges in Converting Deep Learning Models

An Empirical Study of Challenges in Converting Deep Learning Models

28 June 2022
Moses Openja
Amin Nikanjam
Ahmed Haj Yahmed
Foutse Khomh
Zhen Ming
Zhengyong Jiang
    AAML
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Papers citing "An Empirical Study of Challenges in Converting Deep Learning Models"

4 / 4 papers shown
Title
On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance
On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance
Jaskirat Singh
Bram Adams
Ahmed E. Hassan
VLM
26
0
0
01 Nov 2024
A Partial Replication of MaskFormer in TensorFlow on TPUs for the
  TensorFlow Model Garden
A Partial Replication of MaskFormer in TensorFlow on TPUs for the TensorFlow Model Garden
Vishal Purohit
Wenxin Jiang
Akshath R. Ravikiran
James C. Davis
21
1
0
29 Apr 2024
On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance
On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance
Jaskirat Singh
Emad Fallahzadeh
Bram Adams
Ahmed E. Hassan
MQ
32
3
0
25 Mar 2024
Generating Natural Language Adversarial Examples
Generating Natural Language Adversarial Examples
M. Alzantot
Yash Sharma
Ahmed Elgohary
Bo-Jhang Ho
Mani B. Srivastava
Kai-Wei Chang
AAML
230
909
0
21 Apr 2018
1