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Machine learning paper explores uncertainty in dynamical systems

By PulseAugur Editorial · [2 sources] ·

This paper explores the concept of uncertainty in machine learning, specifically focusing on dynamical systems. It differentiates between aleatoric and epistemic uncertainty, which have been extensively studied in supervised and generative modeling. The research aims to provide a machine learning perspective on uncertainty modeling for dynamical systems, an area that has received less attention. AI

IMPACT This research could lead to more robust and reliable AI systems by improving how they handle uncertainty in dynamic environments.

RANK_REASON The cluster contains an academic paper published on arXiv.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Yusuf Sale, Christopher B\"ulte, Felix Czaja, Joshua Stiller, Eyke H\"ullermeier ·

    What Uncertainties Do We Need for Dynamical Systems?

    arXiv:2606.11988v1 Announce Type: cross Abstract: The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also in other settings such as generative modeling. In …

  2. arXiv stat.ML TIER_1 English(EN) · Eyke Hüllermeier ·

    What Uncertainties Do We Need for Dynamical Systems?

    The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also in other settings such as generative modeling. In this paper, we offer a machine learning perspectiv…

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