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Uncertainty

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Structured Ensembles: An approach to reduce the memory footprint of ensemble methods.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a novel ensembling technique for deep neural networks, which is able to drastically reduce the required memory compared to alternative approaches. In particular, we propose to extract multiple sub-networks from a single, unt...

Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm.

Environmental science and pollution research international
Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting d...

Estimation with Uncertainty via Conditional Generative Adversarial Networks.

Sensors (Basel, Switzerland)
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, ...

Uncertainty propagation for dropout-based Bayesian neural networks.

Neural networks : the official journal of the International Neural Network Society
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...

Detecting failure modes in image reconstructions with interval neural network uncertainty.

International journal of computer assisted radiology and surgery
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...

Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction.

IEEE transactions on neural networks and learning systems
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...

Dengue models based on machine learning techniques: A systematic literature review.

Artificial intelligence in medicine
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...

Extremely randomized neural networks for constructing prediction intervals.

Neural networks : the official journal of the International Neural Network Society
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 ...

RBFNN-Based Singularity-Free Terminal Sliding Mode Control for Uncertain Quadrotor UAVs.

Computational intelligence and neuroscience
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 ...

Fuzzy adaptive fault diagnosis and compensation for variable structure hypersonic vehicle with multiple faults.

PloS one
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...