Uncertainty quantification

Science of characterizing uncertainties

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📌 Topics

Artificial Intelligence (2)
Uncertainty Reduction (1)
Model Optimization (1)
Computer Vision (1)
Model Evaluation (1)
AI Safety (1)

🏷️ Keywords

Epistemic uncertainty (1) · Invariant transformation (1) · Resampling (1) · AI inference accuracy (1) · Machine learning (1) · Model optimization (1) · Aleatoric uncertainty (1) · VAUQ (1) · Vision-Language Models (1) · Uncertainty Quantification (1) · Model Evaluation (1) · Hallucination (1) · Self-Evaluation (1) · Computer Vision (1) · AI Safety (1)

📖 Key Information

Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. An example would be to predict the acceleration of a human body in a head-on crash with another car: even if the speed was exactly known, small differences in the manufacturing of individual cars, how tightly every bolt has been tightened, etc., will lead to different results that can only be predicted in a statistical sense.

📰 Related News (2)

🇺🇸 Invariant Transformation and Resampling based Epistemic-Uncertainty Reduction (2026-02-27)
arXiv:2602.23315v1 Announce Type: new Abstract: An artificial intelligence (AI) model can be viewed as a function that maps inputs to outputs in high...
🇺🇸 VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation (2026-02-25)
arXiv:2602.21054v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) frequently hallucinate, limiting their safe deployment in real...

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People and organizations frequently mentioned alongside Uncertainty quantification:

Hallucination · 1 shared articles
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Computer vision · 1 shared articles
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Resampling · 1 shared articles
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Machine learning · 1 shared articles