Results 251 to 260 of about 236,656 (279)

Bayesian evolution of rich neural networks

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005
In this paper we present a genetic approach that uses a Bayesian fitness function to the design of rich neural network topologies in order to find an optimal domain-specific non-linear function approximator with good generalization performance. Rich neural networks have a feed-forward topology with shortcut connections and arbitrary activation ...
MATTEUCCI, MATTEO, D. Spadoni
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Sparse Bayesian Recurrent Neural Networks

2015
Recurrent 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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Bayesian neural networks and density networks

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1995
Abstract This paper reviews the Bayesian approach to learning in neural networks, then introduces a new adaptive model, the density network. This is a neural network for which target outputs are provided, but the inputs are unspecified. When a probability distribution is placed on the unknown inputs, a latent variable model is defined that is capable
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Hierarchical Bayesian neural network

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 2023
Alexis Bensen, Kahana L. Adam, Zerotti L. Woods   +2 more
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Bayesian Neural Networks in Predictive Neurosurgery

"Bayesian Neural Networks in Predictive Neurosurgery" explains both conceptually and theoretically the combination of statistical techniques for clinical prediction models, including artificial neural networks, Bayesian regression, and Bayesian neural networks. This clinical prediction system incorporates both prior knowledge and one's own experiences (
Benjamin W Y, Lo, Hitoshi, Fukuda
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BAYESIAN NEURAL NETWORKS

Statistical Problems in Particle Physics, Astrophysics and Cosmology, 2006
PUSHPALATHA C. BHAT, HARRISON B. PROSPER
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Bayesian Nonparametrics via Neural Networks

Journal of the American Statistical Association, 2006
(2006). Bayesian Nonparametrics via Neural Networks. Journal of the American Statistical Association: Vol. 101, No. 475, pp. 1313-1313.
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Compiling Bayesian Networks into Neural Networks

1993
The criticism on the usage of Bayesian Networks in expert systems was centered around the claim that the use of probability requires a massive amount of data in the form of conditional probabilities. This paper shows that given information easily obtained from experts, the dependence model and some observations, the conditional probabilities can be ...
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A Bayesian Mixture Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

IEEE Transactions on Transportation Electrification, 2022
Shuxin Zhang, Zhitao Liu, Hongye Su
exaly  

Online Downlink Multi-User Channel Estimation for mmWave Systems Using Bayesian Neural Network

IEEE Journal on Selected Areas in Communications, 2021
Nilesh Kumar Jha, Lau, Vincent K N
exaly