27 papers · ranked by Valyu relevance
Christian Lovis, Eman Rezk, Grace Nneji, Alexander Kurz + 8 more
'Katja Hauser' 'Hendrik Alexander Mehrtens' 'Eva Krieghoff-Henning' 'Achim Hekler' 'Jakob Nikolas Kather' 'Stefan Fröhling' 'Christof von Kalle' 'Titus Josef Brinker'] Background Deep neural networks are showing impressive results in different medical image classification tasks. However, for real-world applications…
Samanta Piano
This chapter introduces the fundamental principles of metrology and the concept of measurement uncertainty. It explains the role of measurement in engineering and manufacturing, outlines the distinction between error and uncertainty, and presents standard methods for evaluating uncertainty, including the GUM framework…
Peter Hatfield
Uncertainty quantification is a key part of astronomy and physics; scientific researchers attempt to model both statistical and systematic uncertainties in their data as best as possible, often using a Bayesian framework. Decisions might then be made on the resulting uncertainty quantification perhaps whether or not to…
Shuo Chen
Uncertainty plays a crucial role in the machine learning field. Both model trustworthiness and performance require the understanding of uncertainty, especially for models used in high-stake applications where errors can cause cataclysmic consequences, such as medical diagnosis and autonomous driving. Accordingly…
Hao Tang, Tianle Yue, Ying Li
Machine learning (ML) has become an important technique in materials science, markedly accelerating the discovery and design of novel materials, and concurrently lowering the burden of experimental costs. Uncertainty quantification (UQ) plays a pivotal role in the accurate prediction and innovative design of novel…
Junghwan Mun, Hyukdoo Choi, Ikhlas Abdel-Qader
For object detection, capturing the scale of uncertainty is as important as accurate localization. Without understanding uncertainties, self-driving vehicles cannot plan a safe path. Many studies have focused on improving object detection, but relatively little attention has been paid to uncertainty estimation. We…
Authors not listed
Accurate determination of the metabolic fate of xenobiotics is essential for ensuring their safety and efficacy. While in vivo and in vitro methods remain the gold standard for assessing metabolic properties, they are both costly and time-consuming. In silico metabolism prediction models offer complementary solutions…
Elton Donfack-Siewe, Jérôme Morio, Sylvain Dubreuil, Jean-Philippe Navarro + 1 more
In aerospace certification and other safety-critical domains, conservative quantile estimation such as A- and B-basis values is essential to guarantee reliability. While these metrics are traditionally derived from experimental campaigns, this work focuses on their estimation using a validated deterministic numerical…
Fahimeh Fakour, Ali Mosleh, Ramin Ramezani
Deep Learning Authors: ['Fahimeh Fakour' 'Ali Mosleh' 'Ramin Ramezani'] The adaptation and use of Machine Learning (ML) in our daily lives has led to concerns in lack of transparency, privacy, reliability, among others. As a result, we are seeing research in niche areas such as interpretability, causality, bias and…
Anna Deneer, Jaap Molenaar, Christian Fleck
Uncertainty is ubiquitous in biological systems. These uncertainties can be the result of lack of knowledge or due to a lack of appropriate data. Additionally, the natural variability of biological systems caused by intrinsic noise, e.g. in stochastic gene expression, leads to uncertainties. With the help of numerical…
Xiaolu Wang, Peter Dayan, Paul M Bays
The activity of neural populations typically encodes more information about sensory or motor variables than can be captured by point estimates of the variables. We present and compare two approaches to quantifying this additional or ancillary information and its relationship to uncertainty: the mutual information…
Gongcheng Liu, Huimin Wang, Yanlin Han, Chunlong Liu + 1 more
The measurement uncertainty is a crucial quantitative parameter for assessing the reliability of the result. The study aimed to propose a new budget for uncertainty evaluation of a reference measurement procedure for the determination of total testosterone in human serum. The adaptive Monte Carlo method (aMCM) was used…
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen
With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet…
Nan Chen, Stephen Wiggins, Marios Andreou
Quantification -- A Tutorial for Beginners Authors: ['Nan Chen' 'Stephen Wiggins' 'Marios Andreou'] George Box, a British statistician, wrote the famous aphorism, "All models are wrong, but some are useful." The aphorism acknowledges that models, regardless of qualitative, quantitative, dynamical, or statistical…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Branimir Zauner, Branko Petrinec, Tomislav Bituh, Saša Ceci + 4 more
'Nikola Volarić' 'Aleksandar Včev' 'Andrea Vukoja' 'Dinko Babić'] Title: Abstract We present an overview of the theory of random measurement errors, focusing on the underlying concepts rather than on a strict mathematical formulation. Although the related literature is extensive, one can frequently encounter partly or…
Nathaniel J. Linden, Boris Kramer, Padmini Rangamani
Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and ‘-omics’…
Authors not listed
Bonkowski and De Souza [Sol. Stat. Ionics 429, 116967 (2025)] provide a guide for performing molecular dynamics simulations of ion transport, including methods for estimating diffusion coefficients and their uncertainties from mean-squared displacement (MSD) data. The discussion of uncertainty in estimated diffusion…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen, Janne Cathrin Hetle Aspheim + 39 more
'Janne Cathrin Hetle Aspheim' 'Claudia Battistin' 'Nicola Bulso' 'Hannah M. Christensen' 'Benjamin Cretois' 'Ryan Cubero' 'Iván A. Davidovich' 'Lisa Dickel' 'Benjamin Dunn' 'Etienne Dunn-Sigouin' 'Karin Dyrstad' 'Sigurd Einum' 'Donata Giglio' 'Haakon Gjerløw' 'Amélie Godefroidt' 'Ricardo González-Gil' 'Soledad Gonzalo…
Simon Cramer, Tobias Müller, Robert Schmitt
virtual measurements Authors: ['Simon Cramer' 'Tobias Müller' 'Robert Schmitt'] In the context of industrially mass-manufactured products, quality management is based on physically inspecting a small sample from a large batch and reasoning about the batch's quality conformance. When complementing physical inspections…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Antje Schnarr, Marcel Mertz
It can be argued that there is an ethical requirement to classify correctly what is known and what is unknown in decision situations, especially in the context of biomedicine when risks and benefits have to be assessed. This is because other methods for assessing potential harms and benefits, decision logics and/or…
Arnau Albà, Romana Boiger, Dimitri Rochman, Andreas Adelmann
Uncertainty quantification (UQ) is an active area of research, and an essential technique used in all fields of science and engineering. The most common methods for UQ are Monte Carlo and surrogate-modelling. The former method is dimensionality independent but has slow convergence, while the latter method has been…
Daniel Wallach, Taru Palosuo, Henrike Mielenz, Samuel Buis + 31 more
Crop phenology has a major influence on crop yield and is a major aspect of crop response to global warming. Process-based models of phenology are often used to predict the effect of weather on the development rate of crops through their growth phases, but such models are associated with large uncertainties, as…
Matthew J. Simpson, Oliver J. Maclaren
Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Developing mechanistic insight by combining mathematical models and experimental data is especially critical in mathematical biology as new data and new types of data…
Edward Kroc, Andrea Tangherloni
In this paper, we generalize the notion of measurement error on deterministic sample datasets to accommodate sample data that are random-variable-valued. This leads to the formulation of two distinct kinds of measurement error: intrinsic measurement error, and incidental measurement error. Incidental measurement error…