Results 11 to 20 of about 224,776 (267)
Bayesian Reasoning with Trained Neural Networks [PDF]
Entropy, 2021 We showed how to use trained neural networks to perform Bayesian reasoning in order to solve tasks outside their initial scope. Deep generative models provide prior knowledge, and classification/regression networks impose constraints.
Jakob Knollmüller, Torsten A. Enßlin
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Bayesian continual learning via spiking neural networks
Frontiers in Computational Neuroscience, 2022 Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification.
Nicolas Skatchkovsky +2 more
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Continual Learning Using Bayesian Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Continual learning models allow to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios in which the models are trained using different data with various distributions, neural networks tend to forget the previously learned knowledge.
Honglin Li +3 more
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Bayesian Neural Networks [PDF]
, 2022 In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related and which are due to the neural network.
Charnock, Tom +2 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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Differentiable PAC–Bayes Objectives with Partially Aggregated Neural Networks
Entropy, 2021 We make two related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC–Bayesian setting: (1) we show how averaging over an ensemble of stochastic neural networks enables a new class of partially ...
Felix Biggs, Benjamin Guedj
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Neural Bayesian Network Understudy
, 2022 Bayesian Networks may be appealing for clinical decision-making due to their inclusion of causal knowledge, but their practical adoption remains limited as a result of their inability to deal with unstructured data. While neural networks do not have this limitation, they are not interpretable and are inherently unable to deal with causal structure in ...
Rabaey, Paloma +2 more
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G3: Genes, Genomes, Genetics, 2021 In conventional linear models for whole-genome prediction and genome-wide association studies (GWAS), it is usually assumed that the relationship between genotypes and phenotypes is linear.
Tianjing Zhao, Rohan Fernando, Hao Cheng
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Bayesian Neural Networks for Aroma Classification [PDF]
Journal of Chemical Information and Computer Sciences, 2002 Bayesian Neural Networks (BNNs) are investigated to test their potential to distinguish between different aroma impressions. Special attention is thereby drawn on mixed aroma impressions, resulting from the flavor description of a single compound with more than one aroma quality.
Klocker, Johanna +3 more
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Applied Sciences, 2022 Increasing product requirements in the mechanical engineering industry and efforts to reduce time-to-market demand highly accurate and resource-efficient finite element simulations.
Paul Meißner, Tom Hoppe, Thomas Vietor
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