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<p>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.</p>

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Timely ML

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月18日

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  • Daniel Kuhse,
  • Harun Teper,
  • Christian Hakert,
  • Jian-Jia Chen

摘要

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.