Uncertainty-Calibrated Collaborative Filtering via Heteroscedastic Bayesian Neural Networks [PDF]
EntropyAlthough recommendation systems based on neural collaborative filtering (NCF) can achieve high rating accuracy, the point estimates they generate do not express prediction confidence.
Xiaowei Wang
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Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials [PDF]
Journal of CheminformaticsMachine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods.
Nicholas T. Wimer +3 more
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A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement
LatinX in AI at Computer Vision and Pattern Recognition Conference 2022, 2022 Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue. Uncertainty quantification is required for many applications, and disentangled aleatoric and epistemic uncertainties are best. In this paper, we generalize methods to produce disentangled uncertainties to work with different uncertainty quantification methods,
Matias Valdenegro-Toro +1 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.
Gregory Delipei
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Cyber Defense Effectiveness Evaluation for ICS Under Uncertainty: A Dynamic Bayesian Network Approach with Information Entropy [PDF]
EntropyProactive defense planning in Industrial Control Systems (ICS) is critically constrained by two intertwined types of uncertainty: epistemic uncertainty, arising from the defender’s limited observability of the system state and incomplete knowledge of ...
Rongbao Kang +6 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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Hybridndiff-UQ: Uncertainty quantification for hybrid neural differentiable modeling [PDF]
Theoretical and Applied Mechanics LettersThe hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning. These models, integrating numerical representations of known physics into deep neural networks, offer enhanced predictive capabilities and
Deepak Akhare +2 more
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The aleatoric aspect of literary tourism: an example from Havana
Tourism, 2020 The expression literary tourism is an established term which rarely raises controversies. It fits well into a positivist way of thinking about tourism reality.
Jacek Kaczmarek
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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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Deep Modeling of Non-Gaussian Aleatoric Uncertainty
IEEE Robotics and Automation LettersDeep learning offers promising new ways to accurately model aleatoric uncertainty in robotic state estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian. In this study, we formulate and evaluate three fundamental deep learning approaches for conditional probability ...
Rebecca L Russell +2 more
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