Results 31 to 40 of about 234,090 (282)
Simple Direct Uncertainty Quantification Technique Based on Machine Learning Regression
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Epistemic uncertainty quantification provides useful insight into both deep and shallow neural networks' understanding of the relationships between their training distributions and unseen instances and can serve as an estimate of classification ...
Katherine E. Brown, Douglas A. Talbert
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Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference
Sensors, 2022 In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years.
Monika E. Heringhaus +3 more
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Neural‑Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding [PDF]
, 2018 Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years.
Al Hasan, Mohammad +3 more
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, 2018 This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochastic modeling.
Mullachery, Vikram +2 more
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Using topological data analysis for building Bayesan neural networks
Научно-технический вестник информационных технологий, механики и оптикиFor the first time, a simplified approach to constructing Bayesian neural networks is proposed, combining computational efficiency with the ability to analyze the learning process.
A. S. Vatian +4 more
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Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning
IJAIN (International Journal of Advances in Intelligent Informatics), 2021 The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels.
Wandercleiton Cardoso, Renzo di Felice
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Bayesian Policy Gradients via Alpha Divergence Dropout Inference
, 2011 Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult.
Henderson, Peter +3 more
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Spatial Bayesian neural networks
Spatial Statistics35 pages, 21 ...
Andrew Zammit-Mangion +4 more
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Bayesian Neural Networks for Sparse Coding [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Deep learning is actively used in the area of sparse coding. In current deep sparse coding methods uncertainty of predictions is rarely estimated, thus providing the results that lack the quantitative justification. Bayesian learning provides the way to estimate the uncertainty of predictions in neural networks (NNs) by imposing the prior distributions
Kuzin, D., Isupova, O., Mihaylova, L.
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IEEE AccessCausal inference is an important function of the nervous system. To explore causal inference, Bayesian inference performs as the possible framework, mapping neural implementation onto various cortical areas.
Weisi Liu, Xiaogang Pan
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