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Bayesian Neural Networks and Its Application
2008 Fourth International Conference on Natural Computation, 2008The Bayesian approach provides consistent way to do inference by integrating the evidence from data with prior knowledge from the problem. Bayesian neural networks can overcome the main difficulty of controlling the modelpsilas complexity in modelling building of standard neural network.
Chunling Fan +3 more
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Classification with Bayesian Neural Networks
2006I submitted entries for the two classification problems — “Catalysis” and “Gatineau” — in the Evaluating Predictive Uncertainty Challenge. My entry for Catalysis was the best one; my entry for Gatineau was the third best, behind two similar entries by Nitesh Chawla.
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Bayesian dual neural networks for recommendation
Frontiers of Computer Science, 2019Most traditional collaborative filtering (CF) methods only use the user-item rating matrix to make recommendations, which usually suffer from cold-start and sparsity problems. To address these problems, on the one hand, some CF methods are proposed to incorporate auxiliary information such as user/item profiles; on the other hand, deep neural networks,
Jia He 0001 +4 more
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Biological Cybernetics, 1989
A neural network that uses the basic Hebbian learning rule and the Bayesian combination function is defined. Analogously to Hopfield's neural network, the convergence for the Bayesian neural network that asynchronously updates its neurons' states is proved.
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A neural network that uses the basic Hebbian learning rule and the Bayesian combination function is defined. Analogously to Hopfield's neural network, the convergence for the Bayesian neural network that asynchronously updates its neurons' states is proved.
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Sparse Bayesian Recurrent Neural Networks
2015Recurrent neural networks RNNs have recently gained renewed attention from the machine learning community as effective methods for modeling variable-length sequences. Language modeling, handwriting recognition, and speech recognition are only few of the application domains where RNN-based models have achieved the state-of-the-art performance currently ...
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Neural network classification: a Bayesian interpretation
IEEE Transactions on Neural Networks, 1990The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical ...
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Bayesian learning for neural networks: an algorithmic survey
Artificial Intelligence Review, 2023Alexandros Iosifidis
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Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
Computer Methods in Applied Mechanics and Engineering, 2022Kevin Linka +2 more
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Gradient boosting Bayesian neural networks via Langevin MCMC
Neurocomputing, 2023Rohitash Chandra
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Robust Bayesian Abstraction of Neural Networks
2023 International Conference on Machine Learning and Cybernetics (ICMLC), 2023Amany Alshareef +3 more
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