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https://arxiv.org/abs/2603.19118v1 [2603.19118v1] How Uncertainty Estimation Scales with Sampling in Reasoning Models Abstract page for arXiv paper 2603.19118v1: How Uncertainty Estimation Scales with Sampling in Reasoning Models uncertainty estimation https://www.mdpi.com/1424-8220/23/4/1999 Uncertainty Estimation for Quantitative Agarose Gel Electrophoresis of Nucleic Acids This paper considers the evaluation of uncertainty of quantitative gel electrophoresis. To date, such uncertainty estimation presented in the literature are... agarose gel electrophoresis uncertainty estimation quantitative nucleic acids https://openreview.net/forum?id=iBKWqXCSFA Rethinking Uncertainty Estimation in Natural Language Generation | OpenReview Large Language Models (LLMs) are increasingly employed in real-world applications, driving the need to evaluate the trustworthiness of their generated text. To... natural language generation uncertainty estimation rethinking openreview https://openreview.net/forum?id=HSi4VetQLj Improving Uncertainty Estimation through Semantically Diverse Language Generation | OpenReview Large language models (LLMs) can suffer from hallucinations when generating text. These hallucinations impede various applications in society and industry by... uncertainty estimation language generation improving semantically diverse https://openreview.net/forum?id=9EKHN1jOlA Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs | OpenReview Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural... uncertainty estimation finite state calibration probabilistic rnns https://uwaterloo.ca/waterloo-intelligent-systems-engineering-lab/references/keywords/uncertainty-estimation Reference keyword: uncertainty estimation | Waterloo Intelligent Systems Engineering Lab |... uncertainty estimation intelligent systems reference keyword waterloo https://openreview.net/forum?id=u4QXJbcvx8u Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection | OpenReview In this work, we tailor Laplace approximation for 3D object detection, and propose solutions in Fisher approximation, Bayesian inference, and weight prior... laplace approximation epistemic uncertainty 3d object based https://openreview.net/forum?id=0DpKUzl1Se Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations | OpenReview Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on... uncertainty estimation high dimensional adaptive via https://openreview.net/forum?id=xq1QvViDdW Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation | OpenReview Uncertainty estimation is an important research area to make deep neural networks (DNNs) more trustworthy. While extensive research on uncertainty estimation... uncertainty estimation beyond unimodal generalising neural https://openreview.net/forum?id=r98uvwIs9ec A Geometric Method for Improved Uncertainty Estimation in Real-time | OpenReview Our work puts forward an improved method for uncertainty estimation based on the geometric separation of the training set. in real time uncertainty estimation geometric method improved https://speakerdeck.com/motokimura/sampling-free-epistemic-uncertainty-estimation-using-approximated-variance-propagation-iccv2019-oral Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation (ICCV2019... epistemic uncertainty sampling free estimation using https://openreview.net/forum?id=bFcqKvi4A9&referrer=%5Bthe%20profile%20of%20Maksim%20Zhdanov%5D(%2Fprofile%3Fid%3D~Maksim_Zhdanov2) Unveiling Empirical Pathologies of Laplace Approximation for Uncertainty Estimation | OpenReview Uncertainty estimation is crucial in safety-critical applications, where robust out-of-distribution (OOD) detection is essential. Traditional Bayesian methods,... laplace approximation uncertainty estimation unveiling empirical pathologies https://openreview.net/forum?id=TBKLXswKnO Uncertainty Estimation with Recursive Feature Machines | OpenReview In conventional regression analysis, predictions are typically represented as point estimates derived from covariates. The Gaussian Process (GP) offer a... uncertainty estimation with recursive feature machines openreview https://openreview.net/forum?id=te8iyHjbPQd Understanding the Under-Coverage Bias in Uncertainty Estimation | OpenReview We prove that quantile regression exhibits an inherent under-coverage bias, even in well-specified linear models. the under coverage bias uncertainty estimation understanding openreview https://openreview.net/forum?id=BJxI5gHKDr Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning | OpenReview We highlight the problems with common metrics of in-domain uncertainty and perform a broad study of modern ensembling techniques. in domain uncertainty estimation deep learning pitfalls https://openreview.net/forum?id=r1yXEdkvz Uncertainty Estimation via Stochastic Batch Normalization | OpenReview We propose a probabilistic view on Batch Normalization and an efficient test-time averaging technique for uncertainty estimation in batch-normalized DNNs. uncertainty estimation batch normalization via stochastic openreview https://www.ecmwf.int/en/research/special-projects/spatlh00-2012 Homogenization and uncertainty estimation of historic in situ upper air data | ECMWF uncertainty estimation https://openreview.net/forum?id=w8LMtFY97b Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging | OpenReview Over recent years, deep learning based image registration has achieved impressive accuracy in many domains, including medical imaging and, specifically, human... uncertainty estimation for learning hierarchical based registration https://www.econstor.eu/handle/10419/28390 EconStor: Efficient estimation of forecast uncertainty based on recent forecast errors EconStor is a publication server for scholarly economic literature, provided as a non-commercial public service by the ZBW. based on econstor efficient estimation forecast https://openreview.net/forum?id=VD-AYtP0dve Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language... Semantic entropy is a novel uncertainty estimation method for natural language generation that captures uncertainty over meanings rather than sequences. in natural semantic uncertainty linguistic invariances https://www.atlantis-press.com/proceedings/meep-15/25851227 Uncertainty in Estimation of Anthropogenic Aerosol Indirect Effect | Atlantis Press In this paper, uncertainties in estimating anthropogenic aerosol indirect effects on warm clouds are explored using the recently developed Grid point... indirect effect uncertainty estimation anthropogenic aerosol https://www.nist.gov/publications/standard-reference-materials-value-assignment-and-uncertainty-estimation-selected-light Standard Reference Materials ::Value assignment and uncertainty estimation of selected light stable... standard reference materials value assignment https://hand-uncertainty.github.io/ Learning Correlation-aware Aleatoric Uncertainty for 3D Hand Pose Estimation Learning Correlation-aware Aleatoric Uncertainty for 3D Hand Pose Estimation aleatoric uncertainty hand pose learning correlation aware https://arxiv.org/abs/2105.01200 [2105.01200] Uncertainty quantification and estimation in differential dynamic microscopy Abstract page for arXiv paper 2105.01200: Uncertainty quantification and estimation in differential dynamic microscopy uncertainty quantification 2105 estimation differential dynamic https://arxiv.org/html/2503.05245v4 L-FUSION: Laplacian Fetal Ultrasound Segmentation & Uncertainty Estimation fetal ultrasound fusion laplacian segmentation uncertainty https://www.osti.gov/pages/biblio/1842009-late-day-measurement-excised-branches-results-uncertainty-estimation-two-stomatal-parameters-derived-from-response-curves-populus-deltoides-bartr-populus-nigra Late day measurement of excised branches results in uncertainty in the estimation of two stomatal... The U.S. Department of Energy's Office of Scientific and Technical Information https://arxiv.org/abs/2403.17339 [2403.17339] Measurement Uncertainty Impact on Koopman Operator Estimation of Power System Dynamics Abstract page for arXiv paper 2403.17339: Measurement Uncertainty Impact on Koopman Operator Estimation of Power System Dynamics https://drive.google.com/file/d/1HKkl-zzZje9OtnjodA2oVUSxm--ysjDr/view?usp=sharing [WOAH 2021] Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation.pdf... https://deepai.org/publication/uncertainty-aware-low-rank-q-matrix-estimation-for-deep-reinforcement-learning Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning | DeepAI Nov 19, 2021 - 11/19/21 - Value estimation is one key problem in Reinforcement Learning. Albeit many successes have been achieved by Deep Reinforcement Lear... deep reinforcement learning q matrix https://deepai.org/publication/uncertainty-aware-camera-pose-estimation-from-points-and-lines Uncertainty-Aware Camera Pose Estimation from Points and Lines | DeepAI Jul 8, 2021 - 07/08/21 - Perspective-n-Point-and-Line (PnPL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model fr... points and lines camera pose uncertainty aware estimation https://www.nist.gov/publications/estimation-measurement-uncertainty-small-circular-features-measured-cmms The Estimation of Measurement Uncertainty of Small Circular Features Measured by CMMs | NIST Feb 19, 2017 - This paper examines the measurement uncertainty of small circular features as a function of the sampling strategy, i.e., the number and distribution of measurem measurement uncertainty https://neurips.cc/virtual/2020/public/tutorial_0f190e6e164eafe66f011073b4486975.html NeurIPS 2020 : (Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep... https://aclanthology.org/2024.findings-acl.728/ Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models - ACL... Francesco Tonolini, Nikolaos Aletras, Jordan Massiah, Gabriella Kazai. Findings of the Association for Computational Linguistics: ACL 2024. 2024. https://openreview.net/forum?id=rkxNh1Stvr Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O... Learning to Estimate Point-Prediction Uncertainty and Correct Output in Neural Networks https://www.sintef.no/en/publications/publication/0198cc594284-fe0b86b5-bb96-48cd-8b02-cfcd6ef8011f/ Parameter uncertainty in geological formations, and its impact on CO2 storage capacity estimation -... https://www.census.gov/library/working-papers/1999/demo/bell-01.html Accounting for Uncertainty About Variances in Small Area Estimation Oct 8, 2021 - This paper address uncertainty in small area estimate models. accounting for small area uncertainty variances estimation https://www.preprints.org/manuscript/202203.0240 Towards an Effective Application of Parameter Estimation and Uncertainty Analysis to Mathematical... Groundwater models serve as support tools to among others: assess water resources, evaluate management strategies, design remediation systems and optimize... parameter estimation https://deepai.org/publication/using-monte-carlo-dropout-and-bootstrap-aggregation-for-uncertainty-estimation-in-radiation-therapy-dose-prediction-with-deep-learning-neural-networks Using Monte Carlo dropout and bootstrap aggregation for uncertainty estimation in radiation therapy... Nov 1, 2020 - 11/01/20 - Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for... https://www.usgs.gov/data/parameter-estimation-uncertainty-analysis-and-optimization-pest-family-codes-tutorial-jupyter Parameter Estimation, Uncertainty Analysis, and Optimization with the PEST++ Family of codes:... A series of Jupyter notebooks documenting a self-guided, interactive curriculum for the PEST++ family of software codes for uncertainty analysis, parameter... analysis and optimization parameter estimation https://arxiv.org/abs/2104.00232v1 [2104.00232v1] Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation... Abstract page for arXiv paper 2104.00232v1: Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression... https://www.osti.gov/pages/biblio/2484259-quantifying-uncertainty-state-estimation-mok-fobs-method-via-interval-analysis Quantifying Uncertainty in State Estimation: The MoK-FoBS Method via Interval Analysis (Journal... The U.S. Department of Energy's Office of Scientific and Technical Information https://www.osti.gov/biblio/1323036 Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Transient Nonlinear... Abstract not provided. | OSTI.GOV a posteriori https://deepai.org/publication/on-the-density-estimation-problem-for-uncertainty-propagation-with-unknown-input-distributions On the density estimation problem for uncertainty propagation with unknown input distributions |... Dec 18, 2020 - 12/18/20 - In this article we study the problem of quantifying the uncertainty in an experiment with a technical system. We propose new densi... on the density estimation https://arxiv.org/abs/2109.13913 [2109.13913] $f$-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot... Abstract page for arXiv paper 2109.13913: $f$-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception https://openreview.net/forum?id=hbIpwrdfE1 Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language... Hallucinations are a common issue that undermine the reliability of large language models (LLMs). Recent studies have identified a specific subset of... in the https://www.ojp.gov/library/publications/theory-invertible-and-injective-deep-neural-networks-likelihood-estimation-and Theory of Invertible and Injective Deep Neural Networks for Likelihood Estimation and Uncertainty... The author presents a theory of invertible and injective deep neural networks for likelihood estimation and uncertainty quantification. deep neural networks theory of https://openreview.net/forum?id=ZL6yd6N1S2 Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks | OpenReview While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under... graph neural networks epistemic uncertainty