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Journal articles on the topic 'Aleatoric uncertainty'

Author: Grafiati

Published: 12 July 2022

Last updated: 31 July 2025

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1

Pamungkas, Yayi Wira. "Penggunaan Aturan Ular Tangga dalam Musik Aleatorik Berbasis Serialisme Integral." Journal of Music Science, Technology, and Industry 3, no. 2 (2020): 201–22. http://dx.doi.org/10.31091/jomsti.v3i2.1157.

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Purpose: The author does an experiment by using the rules of snake and ladder to find out and understand how the concept of uncertainty can work in serialism-based aleatoric music: by testing it using the most stringent serialism system, namely the system of integral serialism. Research methods: The process of creating the composition of this artistic research work has five stages, namely the exploration stage, the concept preparation stage, the concept analysis stage, the macro structure preparation stage, and the concept application stage. Results and discussion: The concept of snake and lad

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Berry, Lucas, and David Meger. "Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 6806–14. http://dx.doi.org/10.1609/aaai.v37i6.25834.

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In this work, we demonstrate how to reliably estimate epistemic uncertainty while maintaining the flexibility needed to capture complicated aleatoric distributions. To this end, we propose an ensemble of Normalizing Flows (NF), which are state-of-the-art in modeling aleatoric uncertainty. The ensembles are created via sets of fixed dropout masks, making them less expensive than creating separate NF models. We demonstrate how to leverage the unique structure of NFs, base distributions, to estimate aleatoric uncertainty without relying on samples, provide a comprehensive set of baselines, and de

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Zhang, Wang, Ziwen Martin Ma, Subhro Das, et al. "One Step Closer to Unbiased Aleatoric Uncertainty Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16857–64. http://dx.doi.org/10.1609/aaai.v38i15.29627.

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Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties are usually categorized into aleatoric (data) and epistemic (model) uncertainty. In this paper, we point out that the existing popular variance attenuation method highly overestimates aleatoric uncertainty. To address this issue, we proposed a new estimation method by actively de-noising the observed data. By conducting a broad range of experiments, we demonstrate that our proposed approach provides a much closer ap

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Laves, Max-Heinrich, Sontje Ihler, Jacob F. Fast, Lüder A. Kahrs, and Tobias Ortmaier. "Recalibration of Aleatoric and EpistemicRegression Uncertainty in Medical Imaging." Machine Learning for Biomedical Imaging 1, MIDL 2020 (2021): 1–26. http://dx.doi.org/10.59275/j.melba.2021-a6fd.

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The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show that predictive uncertainty is systematically underestimated. We apply sigma scaling with a single scalar value; a simple, yet effective calibration method for both types of uncertainty. The performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-art convolutional network ar

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Badings, Thom, Licio Romao, Alessandro Abate, and Nils Jansen. "Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14701–10. http://dx.doi.org/10.1609/aaai.v37i12.26718.

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Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters leads to epistemic uncertainty. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. However, the underlying models exclusively capture aleatoric but not epistemic uncertainty, and thus require that model parameters are known precisely. Our contribution to overcoming this restriction is a novel abstraction-based co

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Li, Tianyi, Zhengyuan Chen, Zhen Zhang, et al. "Predicting Stress–Strain Curve with Confidence: Balance Between Data Minimization and Uncertainty Quantification by a Dual Bayesian Model." Polymers 17, no. 4 (2025): 550. https://doi.org/10.3390/polym17040550.

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Driven by polymer processing–property data, machine learning (ML) presents an efficient paradigm in predicting the stress–strain curve. However, it is generally challenged by (i) the deficiency of training data, (ii) the one-to-many issue of processing–property relationship (i.e., aleatoric uncertainty), and (iii) the unawareness of model uncertainty (i.e., epistemic uncertainty). Here, leveraging a Bayesian neural network (BNN) and a recently proposed dual-architected model for curve prediction, we introduce a dual Bayesian model that enables accurate prediction of the stress–strain curve whi

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Yu, Xuanlong, Gianni Franchi, Jindong Gu, and Emanuel Aldea. "Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 6835–43. http://dx.doi.org/10.1609/aaai.v38i7.28508.

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Uncertainty quantification is critical for deploying deep neural networks (DNNs) in real-world applications. An Auxiliary Uncertainty Estimator (AuxUE) is one of the most effective means to estimate the uncertainty of the main task prediction without modifying the main task model. To be considered robust, an AuxUE must be capable of maintaining its performance and triggering higher uncertainties while encountering Out-of-Distribution (OOD) inputs, i.e., to provide robust aleatoric and epistemic uncertainty. However, for vision regression tasks, current AuxUE designs are mainly adopted for alea

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Meinert, Nis, Jakob Gawlikowski, and Alexander Lavin. "The Unreasonable Effectiveness of Deep Evidential Regression." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9134–42. http://dx.doi.org/10.1609/aaai.v37i8.26096.

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There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, notably with the capabilities to disentangle aleatoric and epistemic uncertainties. Despite some empirical success of Deep Evidential Regression (DER), there are important gaps in the mathematical found

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Itkina, Masha. "Perception Beyond Sensors Under Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15716–17. http://dx.doi.org/10.1609/aaai.v35i18.17855.

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My research aims to enable spatiotemporal inference in mobile robot perception systems. Specifically, the proposed thesis presents learning-based approaches to the tasks of behavior prediction and occlusion inference that explicitly account for the associated aleatoric and epistemic uncertainty.

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Mehltretter, M. "JOINT ESTIMATION OF DEPTH AND ITS UNCERTAINTY FROM STEREO IMAGES USING BAYESIAN DEEP LEARNING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 69–78. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-69-2022.

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Abstract. The necessity to identify errors in the context of image-based 3D reconstruction has motivated the development of various methods for the estimation of uncertainty associated with depth estimates in recent years. Most of these methods exclusively estimate aleatoric uncertainty, which describes stochastic effects. On the other hand, epistemic uncertainty, which accounts for simplifications or incorrect assumptions with respect to the formulated model hypothesis, is often neglected. However, to accurately quantify the uncertainty inherent in a process, it is necessary to consider all p

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Krapf, Thomas, Michael Hagn, Paul Miethaner, Alexander Schiller, Lucas Luttner, and Bernd Heinrich. "Piecewise Linear Transformation – Propagating Aleatoric Uncertainty in Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 20456–64. http://dx.doi.org/10.1609/aaai.v38i18.30029.

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Real-world data typically exhibit aleatoric uncertainty which has to be considered during data-driven decision-making to assess the confidence of the decision provided by machine learning models. To propagate aleatoric uncertainty represented by probability distributions (PDs) through neural networks (NNs), both sampling-based and function approximation-based methods have been proposed. However, these methods suffer from significant approximation errors and are not able to accurately represent predictive uncertainty in the NN output. In this paper, we present a novel method, Piecewise Linear T

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Hong, Ming, Jianzhuang Liu, Cuihua Li, and Yanyun Qu. "Uncertainty-Driven Dehazing Network." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 906–13. http://dx.doi.org/10.1609/aaai.v36i1.19973.

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Deep learning has made remarkable achievements for single image haze removal. However, existing deep dehazing models only give deterministic results without discussing the uncertainty of them. There exist two types of uncertainty in the dehazing models: aleatoric uncertainty that comes from noise inherent in the observations and epistemic uncertainty that accounts for uncertainty in the model. In this paper, we propose a novel uncertainty-driven dehazing network (UDN) that improves the dehazing results by exploiting the relationship between the uncertain and confident representations. We first

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Zhong, Z., and M. Mehltretter. "MIXED PROBABILITY MODELS FOR ALEATORIC UNCERTAINTY ESTIMATION IN THE CONTEXT OF DENSE STEREO MATCHING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2021 (June 17, 2021): 17–26. http://dx.doi.org/10.5194/isprs-annals-v-2-2021-17-2021.

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Abstract. The ability to identify erroneous depth estimates is of fundamental interest. Information regarding the aleatoric uncertainty of depth estimates can be, for example, used to support the process of depth reconstruction itself. Consequently, various methods for the estimation of aleatoric uncertainty in the context of dense stereo matching have been presented in recent years, with deep learning-based approaches being particularly popular. Among these deep learning-based methods, probabilistic strategies are increasingly attracting interest, because the estimated uncertainty can be quan

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Pham, Nam, and Sergey Fomel. "Uncertainty and interpretability analysis of encoder-decoder architecture for channel detection." GEOPHYSICS 86, no. 4 (2021): O49—O58. http://dx.doi.org/10.1190/geo2020-0409.1.

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We have adopted a method to understand uncertainty and interpretability of a Bayesian convolutional neural network for detecting 3D channel geobodies in seismic volumes. We measure heteroscedastic aleatoric uncertainty and epistemic uncertainty. Epistemic uncertainty captures the uncertainty of the network parameters, whereas heteroscedastic aleatoric uncertainty accounts for noise in the seismic volumes. We train a network modified from U-Net architecture on 3D synthetic seismic volumes, and then we apply it to field data. Tests on 3D field data sets from the Browse Basin, offshore Australia,

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Wiessner, Paul, Grigor Bezirganyan, Sana Sellami, Richard Chbeir, and Hans-Joachim Bungartz. "Uncertainty-Aware Time Series Anomaly Detection." Future Internet 16, no. 11 (2024): 403. http://dx.doi.org/10.3390/fi16110403.

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Traditional anomaly detection methods in time series data often struggle with inherent uncertainties like noise and missing values. Indeed, current approaches mostly focus on quantifying epistemic uncertainty and ignore data-dependent uncertainty. However, consideration of noise in data is important as it may have the potential to lead to more robust detection of anomalies and a better capability of distinguishing between real anomalies and anomalous patterns provoked by noise. In this paper, we propose LSTMAE-UQ (Long Short-Term Memory Autoencoder with Aleatoric and Epistemic Uncertainty Quan

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Chowdhary, Kamaljit, and Paul Dupuis. "Distinguishing and integrating aleatoric and epistemic variation in uncertainty quantification." ESAIM: Mathematical Modelling and Numerical Analysis 47, no. 3 (2013): 635–62. http://dx.doi.org/10.1051/m2an/2012038.

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Senge, Robin, Stefan Bösner, Krzysztof Dembczyński, et al. "Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty." Information Sciences 255 (January 2014): 16–29. http://dx.doi.org/10.1016/j.ins.2013.07.030.

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Hüllermeier, Eyke, and Willem Waegeman. "Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods." Machine Learning 110, no. 3 (2021): 457–506. http://dx.doi.org/10.1007/s10994-021-05946-3.

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AbstractThe notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particula

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Ghasemi-Naraghi, Zeinab, Ahmad Nickabadi, and Reza Safabakhsh. "LogSE: An Uncertainty-Based Multi-Task Loss Function for Learning Two Regression Tasks." JUCS - Journal of Universal Computer Science 28, no. 2 (2022): 141–59. http://dx.doi.org/10.3897/jucs.70549.

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Multi-task learning (MTL) is a popular method in machine learning which utilizes related information of multi tasks to learn a task more efficiently and accurately. Naively, one can benefit from MTL by using a weighted linear sum of the different tasks loss functions. Manual specification of appropriate weights is difficult and typically does not improve performance, so it is critical to find an automatic weighting strategy for MTL. Also, there are three types of uncertainties that are captured in deep learning. Epistemic uncertainty is related to the lack of data. Heteroscedas- tic aleatoric

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Ghasemi-Naraghi, Zeinab, Ahmad Nickabadi, and Reza Safabakhsh. "LogSE: An Uncertainty-Based Multi-Task Loss Function for Learning Two Regression Tasks." JUCS - Journal of Universal Computer Science 28, no. (2) (2022): 141–59. https://doi.org/10.3897/jucs.70549.

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Multi-task learning (MTL) is a popular method in machine learning which utilizes related information of multi tasks to learn a task more efficiently and accurately. Naively, one can benefit from MTL by using a weighted linear sum of the different tasks loss functions. Manual specification of appropriate weights is difficult and typically does not improve performance, so it is critical to find an automatic weighting strategy for MTL. Also, there are three types of uncertainties that are captured in deep learning. Epistemic uncertainty is related to the lack of data. Heteroscedas- tic aleatoric

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Reddy, Soma Datta, and Sunitha Palissery. "Uncertainty-Aware Seismic Signal Discrimination using Bayesian Convolutional Neural Networks." International Journal on Cybernetics & Informatics 13, no. 5 (2024): 207–18. http://dx.doi.org/10.5121/ijci.2024.130513.

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Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region

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Khanzhina, N. E. "Bayesian losses for homoscedastic aleatoric uncertainty modeling in pollen image detection." Scientific and Technical Journal of Information Technologies, Mechanics and Optics 21, no. 4 (2021): 535–44. http://dx.doi.org/10.17586/2226-1494-2021-21-4-535-544.

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Cheng, Lu. "Demystifying Algorithmic Fairness in an Uncertain World." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22662. http://dx.doi.org/10.1609/aaai.v38i20.30278.

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Significant progress in the field of fair machine learning (ML) has been made to counteract algorithmic discrimination against marginalized groups. However, fairness remains an active research area that is far from settled. One key bottleneck is the implicit assumption that environments, where ML is developed and deployed, are certain and reliable. In a world that is characterized by volatility, uncertainty, complexity, and ambiguity, whether what has been developed in algorithmic fairness can still serve its purpose is far from obvious. In this talk, I will first discuss how to improve algori

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Nezhadettehad, Alireza, Arkady Zaslavsky, Abdur Rakib, and Seng W. Loke. "Uncertainty-Aware Parking Prediction Using Bayesian Neural Networks." Sensors 25, no. 11 (2025): 3463. https://doi.org/10.3390/s25113463.

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Parking availability prediction is a critical component of intelligent transportation systems, aiming to reduce congestion and improve urban mobility. While traditional deep learning models such as Long Short-Term Memory (LSTM) networks have been widely applied, they lack mechanisms to quantify uncertainty, limiting their robustness in real-world deployments. This paper proposes a Bayesian Neural Network (BNN)-based framework for parking occupancy prediction that explicitly models both epistemic and aleatoric uncertainty. Although BNNs have shown promise in other domains, they remain underutil

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Feng, Runhai, Dario Grana, and Niels Balling. "Uncertainty quantification in fault detection using convolutional neural networks." GEOPHYSICS 86, no. 3 (2021): M41—M48. http://dx.doi.org/10.1190/geo2020-0424.1.

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Segmentation of faults based on seismic images is an important step in reservoir characterization. With the recent developments of deep-learning methods and the availability of massive computing power, automatic interpretation of seismic faults has become possible. The likelihood of occurrence for a fault can be quantified using a sigmoid function. Our goal is to quantify the fault model uncertainty that is generally not captured by deep-learning tools. We have used the dropout approach, a regularization technique to prevent overfitting and coadaptation in hidden units, to approximate the Baye

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Rajbhandari, E., N. L. Gibson, and C. R. Woodside. "Quantifying uncertainty with stochastic collocation in the kinematic magentohydrodynamic framework." Journal of Physics: Conference Series 2207, no. 1 (2022): 012007. http://dx.doi.org/10.1088/1742-6596/2207/1/012007.

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Abstract We discuss an efficient numerical method for the uncertain kinematic magnetohydrodynamic system. We include aleatoric uncertainty in the parameters, and then describe a stochastic collocation method to handle this randomness. Numerical demonstrations of this method are discussed. We find that the shape of the parameter distributions affect not only the mean and variance, but also the shape of the solution distributions.

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Heringhaus, Monika E., Yi Zhang, André Zimmermann, and Lars Mikelsons. "Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference." Sensors 22, no. 14 (2022): 5408. http://dx.doi.org/10.3390/s22145408.

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In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees of reliability. The goal of this paper is therefore to present a new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy. The overall predictive performance as well as the uncertaint

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Lyu, Yufeng, Zhenyu Liu, Xiang Peng, Jianrong Tan, and Chan Qiu. "Unified Reliability Measure Method Considering Uncertainties of Input Variables and Their Distribution Parameters." Applied Sciences 11, no. 5 (2021): 2265. http://dx.doi.org/10.3390/app11052265.

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Aleatoric and epistemic uncertainties can be represented probabilistically in mechanical systems. However, the distribution parameters of epistemic uncertainties are also uncertain due to sparsely available or inaccurate uncertainty information. Therefore, a unified reliability measure method that considers uncertainties of input variables and their distribution parameters simultaneously is proposed. The uncertainty information for distribution parameters of epistemic uncertainties could be as a result of insufficient data or interval information, which is represented with evidence theory. The

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Gurevich, Pavel, and Hannes Stuke. "Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty." Neurocomputing 350 (July 2019): 291–306. http://dx.doi.org/10.1016/j.neucom.2019.03.031.

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Weiss, Matthias, Stephan Staudacher, Jürgen Mathes, Duilio Becchio, and Christian Keller. "Uncertainty Quantification for Full-Flight Data Based Engine Fault Detection with Neural Networks." Machines 10, no. 10 (2022): 846. http://dx.doi.org/10.3390/machines10100846.

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Current state-of-the-art engine condition monitoring is based on a minimum of one steady-state data point per flight. Due to the scarcity of available data points, there are difficulties distinguishing between random scatter and an underlying fault introducing a detection latency of several flights. Today’s increased availability of data acquisition hardware in modern aircraft provides continuously sampled in-flight measurements, so-called full-flight data. These full-flight data give access to sufficient data points to detect faults within a single flight, significantly improving the availabi

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Andrianomena, Sambatra, and Sultan Hassan. "Predictive uncertainty on astrophysics recovery from multifield cosmology." Journal of Cosmology and Astroparticle Physics 2023, no. 06 (2023): 051. http://dx.doi.org/10.1088/1475-7516/2023/06/051.

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Abstract We investigate how the constraints on the density parameter (Ωm), the power spectrum amplitude (σ 8) and the supernova feedback parameters (A SN1 and A SN2) vary when exploiting information from multiple fields in cosmology. We make use of a convolutional neural network to retrieve the salient features from different combinations of field maps from IllustrisTNG in the CAMELS project. The fields considered are neutral hydrogen (HI), gas density (Mgas), magnetic fields (B) and gas metallicity (Z). We estimate the predictive uncertainty — sum of the squares of aleatoric and epistemic unc

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Huang, Yingsong, Bing Bai, Shengwei Zhao, Kun Bai, and Fei Wang. "Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6960–69. http://dx.doi.org/10.1609/aaai.v36i6.20654.

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Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks.Recent research usually refers to dynamic noise modeling with model output probabilities and loss values, and then separates clean and noisy samples.These methods have gained notable success. However, unlike cherry-picked data, existing approaches often cannot perform well when facing imbalanced datasets, a common scenario in the real world.We thoroughly investigate this phenomenon and point out two major issues that hinder the performance, i.e., inter-class loss distribution discrepancy

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Vassaux, Maxime, Shunzhou Wan, Wouter Edeling, and Peter V. Coveney. "Ensembles Are Required to Handle Aleatoric and Parametric Uncertainty in Molecular Dynamics Simulation." Journal of Chemical Theory and Computation 17, no. 8 (2021): 5187–97. http://dx.doi.org/10.1021/acs.jctc.1c00526.

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Mehltretter, Max, and Christian Heipke. "Aleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis." ISPRS Journal of Photogrammetry and Remote Sensing 171 (January 2021): 63–75. http://dx.doi.org/10.1016/j.isprsjprs.2020.11.003.

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Granados-Ortiz, F. J., and J. Ortega-Casanova. "Quantifying & analysing mixed aleatoric and structural uncertainty in complex turbulent flow simulations." International Journal of Mechanical Sciences 188 (December 2020): 105953. http://dx.doi.org/10.1016/j.ijmecsci.2020.105953.

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Li, Hua, and Kejiang Zhang. "Development of a fuzzy-stochastic nonlinear model to incorporate aleatoric and epistemic uncertainty." Journal of Contaminant Hydrology 111, no. 1-4 (2010): 1–12. http://dx.doi.org/10.1016/j.jconhyd.2009.10.004.

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Sreeharan, Sreelakshmi, Hui Wang, Keigo Hirakawa, and Beiwen Li. "Aleatoric uncertainty quantification in digital fringe projection systems at a per-pixel basis." Optics and Lasers in Engineering 180 (September 2024): 108315. http://dx.doi.org/10.1016/j.optlaseng.2024.108315.

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Aloisio, Angelo, Yuri De Santis, Dag Pasquale Pasca, Massimo Fragiacomo, and Roberto Tomasi. "Aleatoric and epistemic uncertainty in the overstrength of CLT-to-CLT screwed connections." Engineering Structures 304 (April 2024): 117575. http://dx.doi.org/10.1016/j.engstruct.2024.117575.

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Agrawal, Atul, and Phaedon-Stelios Koutsourelakis. "A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty." Journal of Computational Physics 508 (July 2024): 112982. http://dx.doi.org/10.1016/j.jcp.2024.112982.

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Harnist, Bent, Seppo Pulkkinen, and Terhi Mäkinen. "DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties." Geoscientific Model Development 17, no. 9 (2024): 3839–66. http://dx.doi.org/10.5194/gmd-17-3839-2024.

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Abstract. Precipitation nowcasting (forecasting locally for 0–6 h) serves both public security and industries, facilitating the mitigation of losses incurred due to, e.g., flash floods and is usually done by predicting weather radar echoes, which provide better performance than numerical weather prediction (NWP) at that scale. Probabilistic nowcasts are especially useful as they provide a desirable framework for operational decision-making. Many extrapolation-based statistical nowcasting methods exist, but they all suffer from a limited ability to capture the nonlinear growth and decay of prec

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Paseka, Stanislav, and Daniel Marton. "The Impact of the Uncertain Input Data of Multi-Purpose Reservoir Volumes under Hydrological Extremes." Water 13, no. 10 (2021): 1389. http://dx.doi.org/10.3390/w13101389.

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The topic of uncertainties in water management tasks is a very extensive and highly discussed one. It is generally based on the theory that uncertainties comprise epistemic uncertainty and aleatoric uncertainty. This work deals with the comprehensive determination of the functional water volumes of a reservoir during extreme hydrological events under conditions of aleatoric uncertainty described as input data uncertainties. In this case, the input data uncertainties were constructed using the Monte Carlo method and applied to the data employed in the water management solution of the reservoir:

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Kausik, Ravinath, Augustin Prado, Vasileios-Marios Gkortsas, Lalitha Venkataramanan, Harish Datir, and Yngve Bolstad Johansen. "Dual Neural Network Architecture for Determining Permeability and Associated Uncertainty." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 62, no. 1 (2021): 122–34. http://dx.doi.org/10.30632/pjv62n1-2021a8.

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The computation of permeability is vital for reservoir characterization because it is a key parameter in the reservoir models used for estimating and optimizing hydrocarbon production. Permeability is routinely predicted as a correlation from near-wellbore formation properties measured through wireline logs. Several such correlations, namely Schlumberger-Doll Research (SDR) permeability and Timur-Coates permeability models using nuclear magnetic resonance (NMR) measurements, K-lambda using mineralogy, and other variants, have often been used, with moderate success. In addition to permeability,

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Wu, S., M. Heitzler, and L. Hurni. "A CLOSER LOOK AT SEGMENTATION UNCERTAINTY OF SCANNED HISTORICAL MAPS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 1, 2022): 189–94. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-189-2022.

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Abstract. Before modern earth observation techniques came into being, historical maps are almost the exclusive source to retrieve geo-spatial information on Earth. In recent years, the use of deep learning for historical map processing has gained popularity to replace tedious manual labor. However, neural networks, often referred to as “black boxes”, usually generate predictions not well calibrated for indicating if the predictions are trustworthy. Considering the diversity in designs and the graphic defects of scanned historical maps, uncertainty estimates can benefit us in deciding when and

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Kuzucu, Selim, Jiaee Cheong, Hatice Gunes, and Sinan Kalkan. "Uncertainty as a Fairness Measure." Journal of Artificial Intelligence Research 81 (October 13, 2024): 307–35. http://dx.doi.org/10.1613/jair.1.16041.

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Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means for an ML model to be fair. This has been addressed by the ML community with various measures of fairness that depend on the prediction outcomes of the ML models, either at the group-level or the individual-level. These fairness measures are limited in that they utilize point predictions, neglecting their variances, or uncertainties, making them susceptible to noise, missingness and shifts in data. In this paper, we f

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Brake, M. R. "The role of epistemic uncertainty of contact models in the design and optimization of mechanical systems with aleatoric uncertainty." Nonlinear Dynamics 77, no. 3 (2014): 899–922. http://dx.doi.org/10.1007/s11071-014-1350-0.

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Alharbi, Mohammed, and Hassan A. Karimi. "Context-Aware Sensor Uncertainty Estimation for Autonomous Vehicles." Vehicles 3, no. 4 (2021): 721–35. http://dx.doi.org/10.3390/vehicles3040042.

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Sensor uncertainty significantly affects the performance of autonomous vehicles (AVs). Sensor uncertainty is predominantly linked to sensor specifications, and because sensor behaviors change dynamically, the machine learning approach is not suitable for learning them. This paper presents a novel learning approach for predicting sensor performance in challenging environments. The design of our approach incorporates both epistemic uncertainties, which are related to the lack of knowledge, and aleatoric uncertainties, which are related to the stochastic nature of the data acquisition process. Th

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Davey, Timothy. "Incoherence: A Generalized Measure of Complexity to Quantify Ensemble Divergence in Multi-Trial Experiments and Simulations." Entropy 26, no. 8 (2024): 683. http://dx.doi.org/10.3390/e26080683.

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Complex systems pose significant challenges to traditional scientific and statistical methods due to their inherent unpredictability and resistance to simplification. Accurately detecting complex behavior and the uncertainty which comes with it is therefore essential. Using the context of previous studies, we introduce a new information-theoretic measure, termed “incoherence”. By using an adapted Jensen-Shannon Divergence across an ensemble of outcomes, we quantify the aleatoric uncertainty of the system. First we compared this measure to established statistical tests using both continuous and

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Busk, Jonas, Peter Bjørn Jørgensen, Arghya Bhowmik, Mikkel N. Schmidt, Ole Winther, and Tejs Vegge. "Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks." Machine Learning: Science and Technology 3, no. 1 (2021): 015012. http://dx.doi.org/10.1088/2632-2153/ac3eb3.

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Abstract Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are important for assessing confidence in predictions and enabling decision making. However, machine learning models can produce badly calibrated uncertainty estimates and it is therefore crucial to detect and handle uncertainty carefully. In this work we extend a message passing neural network designed specifically for predicting properties of molecules and materials with a calibrated probabilistic predictive distr

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Wang, Guotai, Wenqi Li, Michael Aertsen, Jan Deprest, Sébastien Ourselin, and Tom Vercauteren. "Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks." Neurocomputing 338 (April 2019): 34–45. http://dx.doi.org/10.1016/j.neucom.2019.01.103.

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Penn, Matthew J., Daniel J. Laydon, Joseph Penn, et al. "Intrinsic randomness in epidemic modelling beyond statistical uncertainty." Communications Physics 6, no. 1 (2023). http://dx.doi.org/10.1038/s42005-023-01265-2.

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AbstractUncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty. We only ever observe a single epidemic, and therefore cannot empirically determine aleatoric uncertainty. Here, we characterise both epistemic and aleatoric uncertainty using a time-varying general branching process. Our framework explicitly decomposes aleatoric variance into mechanistic components, quantifying the contribution to uncertainty produced by each factor in

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