Results 241 to 250 of about 434,301 (280)

Bayesian Distillation of Deep Learning Models

Automation and Remote Control, 2021
The authors present a Bayesian approach to teacher-student networks' knowledge distillation. Knowledge distillation was first proposed by \textit{G. Hinton} et al. in their paper [``Distilling the knowledge in a neural network'', Preprint, \url{arXiv:1503.02531}].
Grabovoy, A. V., Strijov, V. V.
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Learning overhypotheses with hierarchical Bayesian models

Developmental Science, 2007
AbstractInductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired.
Kemp, C., Perfors, A., Tenenbaum, J.
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Bayesian Transfer Learning Between Uniformly Modelled Bayesian Filters

2020
We investigate sensor network nodes that sequentially infer states with bounded values, and affected by noise that is also bounded. The transfer of knowledge between such nodes is the principal focus of this chapter. A fully Bayesian framework is adopted, in which the source knowledge is represented by a bounded data predictor, the specification of a ...
Ladislav Jirsa, Lenka Kuklišová Pavelková, Anthony Quinn   +2 more
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Bayesian Model Learning Based on Predictive Entropy

Journal of Logic, Language and Information, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Corander, Jukka, Marttinen, Pekka
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Efficient variational Bayesian model updating by Bayesian active learning

Mechanical Systems and Signal Processing
As a main task of inverse problem, model updating has received more and more attention in the area of inspection, sensing, and monitoring technologies during the recent decades, where the estimation of posterior probability density function (PDF) of unknown model parameters is still challenging for expensive-to-evaluate models of interest.
Pengfei Wei, Sifeng Bi, Michael Beer
exaly   +3 more sources

Modelling Analysts’ Recommendations via Bayesian Machine Learning

SSRN Electronic Journal, 2018
We apply state-of-the-art Bayesian machine learning to test whether we can extract valuable information from analysts’ recommendations of stock performance. We use a probabilistic model for independent Bayesian classifier combination that has been successfully applied in both the physical and biological sciences. The technique is ideally suited for the
David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor, Andrew Sinclair   +4 more
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Bayesian Modelling for Machine Learning

2005
Learning algorithms are central to pattern recognition, artificial intelligence, machine learning, data mining, and statistical learning. The term often implies analysis of large and complex data sets with minimal human intervention. Bayesian learning has been variously described as a method of updating opinion based on new experience, updating ...
Paul Rippon, Kerrie Mengersen
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Learning tractable NAT-modeled Bayesian networks

Annals of Mathematics and Artificial Intelligence, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang Xiang, Qian Wang
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Bayesian learning of speech duration models

IEEE Transactions on Speech and Audio Processing, 2003
This paper presents the Bayesian speech duration modeling and learning for hidden Markov model (HMM) based speech recognition. We focus on the sequential learning of HMM state duration using quasi-Bayes (QB) estimate. The adapted duration models are robust to nonstationary speaking rates and noise conditions.
null Jen-Tzung Chien, null Chih-Hsien Huang   +1 more
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Recursive Bayesian Regression Modeling and Learning

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
This paper presents a new Bayesian regression and learning algorithm for adaptive pattern classification. Our aim is to continuously update regression parameters to meet nonstationary environments for real-world applications. Here, a kernel regression model is used to represent two-class data.
Jen-Tzung Chien, Jung-Chun Chen
openaire   +1 more source