Timely {ML}
We propose two complementary research directions, “Time for {ML}” and “{ML} for Time”, that we believe to be critical for the deployment of machine-learning ({ML}) applications in time-sensitive applications. “Time for {ML}” refers to {ML} systems that are aware of and can adapt to dynamic time constraints regarding their execution, while “{ML} for Time” refers to {ML} systems that are aware of and can deal with data’s temporal aspects, such as misalignment. We believe these two directions are complementary and can be combined to provide more robust and reliable machine learning systems.
- Published in:
Real-Time Systems - Type:
Article - Authors:
- Year:
2025 - Source:
https://doi.org/10.1007/s11241-025-09449-5
Citation information
: Timely {ML}, Real-Time Systems, 2025, June, https://doi.org/10.1007/s11241-025-09449-5, Kuhse.etal.2025b,
@Article{Kuhse.etal.2025b,
author={Kuhse, Daniel; Teper, Harun; Hakert, Christian; Chen, Jian-Jia},
title={Timely {ML}},
journal={Real-Time Systems},
month={June},
url={https://doi.org/10.1007/s11241-025-09449-5},
year={2025},
abstract={We propose two complementary research directions, “Time for {ML}” and “{ML} for Time”, that we believe to be critical for the deployment of machine-learning ({ML}) applications in time-sensitive applications. “Time for {ML}” refers to {ML} systems that are aware of and can adapt to dynamic time constraints regarding their execution, while “{ML} for Time” refers to {ML} systems...}}