Test & Evaluation Best Practices for Machine Learning-Enabled Systems

10 October 2023

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 17 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