SSRN Electronic Journal, 2016 Democracy usually is identified by the right to vote. However, in recent times voting procedures have been criticized, as they seemingly do not guarantee that all parts of the population have an adequate voice in the established political process.
Frey, Bruno S., Osterloh, Margit
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UbiQTree: Uncertainty quantification in XAI with tree ensembles [PDF]
PatternsSummary: Explainable artificial intelligence (XAI) techniques, particularly Shapley additive explanations (SHAP), are essential for interpreting ensemble tree-based models in critical areas such as healthcare.
Akshat Dubey +3 more
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Frontiers in Energy Research, 2023 Fuel performance modeling and simulation includes many uncertain parameters from models to boundary conditions, manufacturing parameters and material properties.
Kostadin Ivanov
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Towards a more reliable assessment of aortic diameters using a Bayesian Z-score [PDF]
Scientific ReportsThe Z-score is a conceptually simple and widely adopted standard for assessing aortic dilatation from echocardiographic measurements. It is routinely used to monitor patient progression and schedule follow-up checks. However, several criticisms have been
Luca Bindini +7 more
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Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT–2 SAR Images and CNNs
Remote Sensing, 2022 With the increasing availability of SAR imagery in recent years, more research is being conducted using deep learning (DL) for the classification of ice and open water; however, ice and open water classification using conventional DL methods such as ...
Nastaran Saberi, Claude R Duguay
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Data-driven organic solubility prediction at the limit of aleatoric uncertainty [PDF]
Nature CommunicationsSmall molecule solubility is a critically important property which affects the efficiency, environmental impact, and phase behavior of synthetic processes.
Lucas Attia +3 more
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Improved multi-objective decision-making in manufacturing processes through uncertainty quantification and robust pareto front modelling [PDF]
Scientific ReportsManufacturing processes often exhibit complex relationships between input parameters and output responses, posing challenges for optimization and decision-making.
Arne De Temmerman, Mathias Verbeke
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Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
IEEE Transactions on Big Data, 2023 Historical interactions are the default choice for recommender model training, which typically exhibit high sparsity, i.e., most user-item pairs are unobserved missing data. A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions.
Chenxu Wang, Fuli Feng, Yang Zhang
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Uncertainty-Aware Parking Prediction Using Bayesian Neural Networks [PDF]
SensorsParking 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 ...
Alireza Nezhadettehad +3 more
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Cross-Modal Interaction Fusion-Based Uncertainty-Aware Prediction Method for Industrial Froth Flotation Concentrate Grade by Using a Hybrid SKNet-ViT Framework [PDF]
SensorsIn froth flotation, the features of froth images are important information to predict the concentrate grade. However, the froth structure is influenced by multiple factors, such as air flowrate, slurry level, ore properties, reagents, etc., which leads ...
Fanlei Lu +4 more
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