Data Models for Dataset Drift Controls in Machine Learning with Optical Images
Abstract
This study addresses robustness concerns in machine learning due to dataset drift by integrating physical optics with machine learning to create explicit, differentiable data models. These models illuminate the impact of data generation on model performance and facilitate drift synthesis, precise tolerancing of model sensitivity (drift forensics), and beneficial drift creation (drift optimization). Accompanying the study are two datasets, Raw-Microscopy and Raw-Drone, available at https://github.com/aiaudit-org/raw2logit. *Note:* The full-length archival version of this manuscript can be found in the *Transactions on Machine Learning Research* (TMLR) at https://openreview.net/forum?id=I4IkGmgFJz.
Cite
Text
Oala et al. "Data Models for Dataset Drift Controls in Machine Learning with Optical Images." ICML 2023 Workshops: Differentiable_Almost_Everything, 2023.Markdown
[Oala et al. "Data Models for Dataset Drift Controls in Machine Learning with Optical Images." ICML 2023 Workshops: Differentiable_Almost_Everything, 2023.](https://mlanthology.org/icmlw/2023/oala2023icmlw-data/)BibTeX
@inproceedings{oala2023icmlw-data,
title = {{Data Models for Dataset Drift Controls in Machine Learning with Optical Images}},
author = {Oala, Luis and Aversa, Marco and Nobis, Gabriel and Willis, Kurt and Neuenschwander, Yoan and Buck, Michèle and Matek, Christian and Extermann, Jerome and Pomarico, Enrico and Samek, Wojciech and Murray-Smith, Roderick and Clausen, Christoph and Sanguinetti, Bruno},
booktitle = {ICML 2023 Workshops: Differentiable_Almost_Everything},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/oala2023icmlw-data/}
}