Results 31 to 40 of about 3,905,952 (289)
Prediction of concrete fatigue durability using Bayesian neural networks
Computer Assisted Methods in Engineering and Science, 2022 The utility of Bayesian neural networks to predict concrete fatigue durability as a function of concrete mechanical parameters of a specimen and characteristics of the loading cycle is investigated.
Marek Słoński
doaj
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
Advances in Neural Information Processing Systems 36, 2023 A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential information of a large-scale dataset and thus can be used as a proxy dataset for scalable Bayesian inference. Typically, a Bayesian pseudocoreset is constructed by minimizing a divergence measure between the posterior conditioning on the pseudocoreset and the posterior ...
Balhae Kim, Hyungi Lee, Juho Lee 0001
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A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
CoRR, 2023 Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process (GP) surrogate models which are easy to optimize and support exact inference.
Yucen Lily Li +2 more
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Active and transfer learning with partially Bayesian neural networks for materials and chemicals† [PDF]
Digital DiscoveryActive learning, an iterative process of selecting the most informative data points for exploration, is crucial for efficient characterization of materials and chemicals property space.
Sarah I. Allec, Maxim Ziatdinov
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Output Partitioning of Neural Networks [PDF]
, 2005 Many constructive learning algorithms have been proposed to find an appropriate network structure for a classification problem automatically. Constructive learning algorithms have drawbacks especially when used for complex tasks and modular approaches ...
Yinan, Q. +11 more
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Bayesian Neural Network Ensembles
CoRR, 2018 Ensembles of neural networks (NNs) have long been used to estimate predictive uncertainty; a small number of NNs are trained from different initialisations and sometimes on differing versions of the dataset. The variance of the ensemble's predictions is interpreted as its epistemic uncertainty.
Tim Pearce, Mohamed Zaki, Andy Neely
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Articulatory feature recognition using dynamic Bayesian networks [PDF]
, 2007 We describe a dynamic Bayesian network for articulatory feature recognition. The model is intended to be a component of a speech recognizer that avoids the problems of conventional ``beads-on-a-string'' phoneme-based models. We demonstrate that the model
Simon King +8 more
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Bayesian non-linear matching of pairwise microarray gene expressions [PDF]
, 2008 In this paper, we present a Bayesian non-linear model to analyze matching pairs of microarray expression data. This model generalizes, in terms of neural networks, standard linear matching models. As a practical application, we analyze data of patients
Marín Díazaraque, Juan Miguel +1 more
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Bayesian Graph Convolutional Neural Networks via Tempered MCMC
IEEE Access, 2021 Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data.
Rohitash Chandra +3 more
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Bayesian Recurrent Neural Networks
CoRR, 2017 In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Meire Fortunato +2 more
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