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The motivations for using variational inference (VI) in neural networks differ significantly from those in latent variable models.
Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
Geoffrey E. Hinton and Drew Van Camp · 1993
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Bayesian Learning for Neural Networks
Radford M. Neal · 1995
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Ensemble learning for multi-layer networks
D Barber and C M Bishop · 1998
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An Introduction to Variational Methods for Graphical Models An Introduction to Variational Methods for Graphical Models
Michael I Jordan, Tommi S Jaakkola, Lawrence K Saul, and Florham Park · 1998
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Practical Variational Inference for Neural Networks
Alex Graves · 2011
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Two problems with variational expectation maximisation for time-series models
Richard E Turner and Maneesh Sahani · 2011
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Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann LeCun, and Rob Fergus · 2013
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Sida I Wang and Christopher D Manning · 2013
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Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Dropout as a Bayesian Approximation : Representing Model Uncertainty in Deep Learning
Yarin Gal and Zoubin Ghahramani · 2015
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Variational Dropout and the Local Reparameterization Trick
Diederik P Kingma, Tim Salimans, and Max Welling · 2015
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Thang D Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, and Richard E Turner · 2016
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Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Christos Louizos and Max Welling · 2016
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Bayesian Compression for Deep Learning, 2017
Christos Louizos, Karen Ullrich, and Max Welling · 2017
Later among the works it cites.
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos and Max Welling · 2017
Later among the works it cites.
Variational Dropout Sparsifies Deep Neural Networks
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Cited alongside, same era.
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
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