Results 331 to 340 of about 3,213,103 (353)

A Bayesian Deep Learning Framework for RUL Prediction Incorporating Uncertainty Quantification and Calibration

IEEE Transactions on Industrial Informatics, 2022
In this article, deep learning (DL) has attracted increasing attention for remaining useful life (RUL) prediction. However, most DL-based prognostics methods only provide deterministic RUL values while ignoring the associated epistemic and aleatoric ...
Yan-Hui Lin, Gang Li
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

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 Quantification

2021
Behnam Sadeghi, Eric Grunsky, Vera Pawlowsky-Glahn   +2 more
  +4 more sources

MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty

North American Chapter of the Association for Computational Linguistics
Despite the massive advancements in large language models (LLMs), they still suffer from producing plausible but incorrect responses. To improve the reliability of LLMs, recent research has focused on uncertainty quantification to predict whether a ...
Yongjin Yang, Haneul Yoo, Hwaran Lee
semanticscholar   +1 more source

Label-wise Aleatoric and Epistemic Uncertainty Quantification

Conference on Uncertainty in Artificial Intelligence
We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows uncertainty to be quantified at the individual class level, thereby improving ...
Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier   +5 more
semanticscholar   +1 more source

Model Uncertainty Quantification

2015
Uncertainty quantification (UQ) is the analytic process of determining the effect of input uncertainties (both their magnitudes and sources) on system outcomes. Traditionally applied in engineering reliability analysis, UQ now plays a significant role in environmental and water resource (EWR) applications as environmental engineers and modelers are ...
Ne-Zheng Sun, Alexander Sun
openaire   +1 more source

Objective Uncertainty Quantification

2019
When designing an operator to alter the behavior of a physical system, the standard engineering paradigm is to begin with a scientific model describing the system, mathematically characterize a class of operators, define a performance cost relative to the operational objective, and pick an operator that minimizes the performance cost.
Edward R. Dougherty, Lori A. Dalton, Roozbeh Dehghannasiri   +2 more
openaire   +1 more source

Uncertainty Quantification of Derivative Instruments

SSRN Electronic Journal, 2015
AbstractModel and parameter uncertainties are common whenever some parametric model is selected to value a derivative instrument. Combining the Monte Carlo method with the Smolyak interpolation algorithm, we propose an accurate efficient numerical procedure to quantify the uncertainty embedded in complex derivatives. Except for the value function being
Sun, Xianming, Vanmaele, Michèle
openaire   +1 more source