Timely ML
Daniel Kuhse, Harun Teper, Christian Hakert, Jian-Jia Chen · Real-Time Systems · 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 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.