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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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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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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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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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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Uncertainty-aware and causal test-time adaptive foundation model for robust colorectal cancer pathology diagnosis [PDF]
npj Digital MedicineColorectal cancer (CRC) is a leading malignancy worldwide, where histopathological assessment of hematoxylin and eosin (H&E) stained whole-slide images remains the diagnostic gold standard.
Shenghan Lou +15 more
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JOINT ESTIMATION OF DEPTH AND ITS UNCERTAINTY FROM STEREO IMAGES USING BAYESIAN DEEP LEARNING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 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.
M. Mehltretter
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MIXED PROBABILITY MODELS FOR ALEATORIC UNCERTAINTY ESTIMATION IN THE CONTEXT OF DENSE STEREO MATCHING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 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.
Z. Zhong, M. Mehltretter
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Bayesian losses for homoscedastic aleatoric uncertainty modeling in pollen image detection [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2021 The paper investigates the homoscedastic aleatoric uncertainty modeling for the detection of pollen in images. The new uncertainty modeling loss functions are presented, which are based on the focal and smooth L1 losses.
Natalia E. Khanzhina
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