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Test & Evaluation Best Practices for Machine Learning-Enabled Systems
10 October 2023
Jaganmohan Chandrasekaran
Tyler Cody
Nicola McCarthy
Erin Lanus
Laura J. Freeman
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Papers citing
"Test & Evaluation Best Practices for Machine Learning-Enabled Systems"
7 / 7 papers shown
Title
A Guide to Failure in Machine Learning: Reliability and Robustness from Foundations to Practice
Eric Heim
Oren Wright
David Shriver
OOD
FaML
63
0
0
01 Mar 2025
Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision
Wenxin Jiang
Vishnu Banna
Naveen Vivek
Abhinav Goel
Nicholas Synovic
George K. Thiruvathukal
James C. Davis
VLM
26
18
0
13 Mar 2023
Differential testing for machine learning: an analysis for classification algorithms beyond deep learning
Steffen Herbold
Steffen Tunkel
15
4
0
25 Jul 2022
An Empirical Study of Challenges in Converting Deep Learning Models
Moses Openja
Amin Nikanjam
Ahmed Haj Yahmed
Foutse Khomh
Zhen Ming
Zhengyong Jiang
AAML
29
19
0
28 Jun 2022
Automatically detecting data drift in machine learning classifiers
Samuel Ackerman
Orna Raz
Marcel Zalmanovici
Aviad Zlotnick
15
34
0
10 Nov 2021
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
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
217
674
0
19 Oct 2020
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