Interoperability in Deep Learning: A User Survey and Failure Analysis of ONNX Model Converters
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1.
Purdue University West Lafayette
Description
This is the artifact for Interoperability in Deep Learning: A User Survey and Failure Analysis of ONNX Model Converters, that will be presented at the 2024 International Symposium on Software Testing and Analysis (ISSTA).
Instructions are in the README.
Please cite:
@inproceedings{jajal2024analysisfailuresrisksdeep,
title={Interoperability in Deep Learning: A User Survey and Failure Analysis of {ONNX} Model Converters},
author={Purvish Jajal and Wenxin Jiang and Arav Tewari and Erik Kocinare and Joseph Woo and Anusha Sarraf and Yung- Hsiang Lu and George K. Thiruvathukal and James C. Davis},
year={2024},
booktitle={Proceedings of the 33nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)},
url={https://arxiv.org/abs/2303.17708},
}
Files
ISSTA_24__Analysis_of_Failures_in_Deep_Learning_Interoperability_A_Case_Study_in_the_ONNX_Ecosystem_appendix.pdf
Files
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Additional details
Funding
- Cisco Systems (United States)
- Google (United States)
- U.S. National Science Foundation
- Transfer Learning using Transformation among Models and Samples 1813935
- U.S. National Science Foundation
- Advancing Low-Power Computer Vision at the Edge 2107020
- U.S. National Science Foundation
- Advancing Low-Power Computer Vision at the Edge 2107230
- U.S. National Science Foundation
- Cyber Infrastructure to Enable Computer Vision Applications at the Edge Using Automated Contextual Analysis 2104319
Dates
- Accepted
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2024-07
Software
- Repository URL
- https://github.com/asub0/fonnx