Results 311 to 314 of about 3,113,242 (314)

Uncertainty quantification for data-driven weather models

Artificial Intelligence for the Earth Systems
Artificial intelligence (AI)-based data-driven weather forecasting models have experienced rapid progress over the last years. Recent studies, with models trained on reanalysis data, achieve impressive results and demonstrate substantial improvements ...
C. Bulte, Nina Horat, J. Quinting, Sebastian Lerch   +3 more
semanticscholar   +1 more source

Uncertainty Quantification on Clinical Trial Outcome Prediction

arXiv.org
The importance of uncertainty quantification is increasingly recognized in the diverse field of machine learning. Accurately assessing model prediction uncertainty can help provide deeper understanding and confidence for researchers and practitioners ...
Tianyi Chen, Nan Hao, Yingzhou Lu, Capucine Van Rechem   +3 more
semanticscholar   +1 more source

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks

Physica A: Statistical Mechanics and its Applications
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression.
Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin   +4 more
semanticscholar   +1 more source

Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach

arXiv.org
In this paper, we study the problem of uncertainty estimation and calibration for LLMs. We begin by formulating the uncertainty estimation problem, a relevant yet underexplored area in existing literature.
Linyu Liu, Yu Pan, Xiaocheng Li, Guanting Chen   +3 more
semanticscholar   +1 more source