AIMC Topic: Uncertainty

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Estimation with Uncertainty via Conditional Generative Adversarial Networks.

Sensors (Basel, Switzerland) Sep 15, 2021
Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, ...
Neural Networks, Computer Uncertainty
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Uncertainty propagation for dropout-based Bayesian neural networks.

Neural networks : the official journal of the International Neural Network Society Sep 9, 2021
Uncertainty evaluation is a core technique when deep neural networks (DNNs) are used in real-world problems. In practical applications, we often encounter unexpected samples that have not seen in the training process. Not only achieving the high-pred...
Uncertainty Monte Carlo Method Neural Networks, Computer Bayes Theorem Reproducibility of Results
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Detecting failure modes in image reconstructions with interval neural network uncertainty.

International journal of computer assisted radiology and surgery Sep 4, 2021
PURPOSE: The quantitative detection of failure modes is important for making deep neural networks reliable and usable at scale. We consider three examples for common failure modes in image reconstruction and demonstrate the potential of uncertainty q...
Humans Image Processing, Computer-Assisted Uncertainty Tomography, X-Ray Computed Neural Networks, Computer
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Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction.

IEEE transactions on neural networks and learning systems Aug 31, 2021
Electronic health records (EHRs) are characterized as nonstationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missi...
Deep Learning Hospital Mortality Electronic Health Records Predictive Value of Tests Stochastic Processes Computational Biology Humans Algorithms Neural Networks, Computer Uncertainty
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Dengue models based on machine learning techniques: A systematic literature review.

Artificial intelligence in medicine Aug 24, 2021
BACKGROUND: Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnost...
Dengue Machine Learning Uncertainty Humans Linear Models
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Extremely randomized neural networks for constructing prediction intervals.

Neural networks : the official journal of the International Neural Network Society Aug 19, 2021
The aim of this paper is to propose a novel prediction model based on an ensemble of deep neural networks adapting the extremely randomized trees method originally developed for random forests. The extra-randomness introduced in the ensemble reduces ...
Algorithms Monte Carlo Method Probability Neural Networks, Computer Uncertainty
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RBFNN-Based Singularity-Free Terminal Sliding Mode Control for Uncertain Quadrotor UAVs.

Computational intelligence and neuroscience Aug 19, 2021
In this article, a singularity-free terminal sliding mode (SFTSM) control scheme based on the radial basis function neural network (RBFNN) is proposed for the quadrotor unmanned aerial vehicles (QUAVs) under the presence of inertia uncertainties and ...
Uncertainty Neural Networks, Computer
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Fuzzy adaptive fault diagnosis and compensation for variable structure hypersonic vehicle with multiple faults.

PloS one Aug 13, 2021
Based on the type-II fuzzy logic, this paper proposes a robust adaptive fault diagnosis and fault-tolerant control (FTC) scheme for multisensor faults in the variable structure hypersonic vehicles with parameter uncertainties. Type-II fuzzy method ap...
Uncertainty Computer Simulation Unmanned Aerial Devices Models, Statistical Humans Fuzzy Logic Nonlinear Dynamics
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Active sensing with artificial neural networks.

Neural networks : the official journal of the International Neural Network Society Aug 12, 2021
The fitness of behaving agents depends on their knowledge of the environment, which demands efficient exploration strategies. Active sensing formalizes exploration as reduction of uncertainty about the current state of the environment. Despite strong...
Neural Networks, Computer Uncertainty
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A stochastic modeling approach for analyzing water resources systems.

Journal of contaminant hydrology Aug 8, 2021
Many uncertain factors exist in the water resource systems, leading to dynamic characteristics of the water distribution process. Especially for the watershed including irrigation area with multiple water sources and water users, it is complicated th...
Uncertainty Water Resources Water Supply Fuzzy Logic Stochastic Processes Probability Models, Theoretical
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