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Bayesian Neural Networks (BNNs) have recently received increasing attention for their ability to provide well-calibrated posterior uncertainties.
Optimal brain damage
Y. LeCun, J. S. Denker, and S. A. Solla · 1990
Earlier work this paper cites.
Bayesian back-propagation
W. L. Buntine and A. S. Weigend · 1991
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A practical Bayesian framework for backpropagation networks
D. J. MacKay · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
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Optimal brain surgeon and general network pruning
B. Hassibi, D. G. Stork, and G. J. Wolff · 1993
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Bayesian learning via stochastic dynamics
R. M. Neal · 1993
Earlier work this paper cites.
Handling sparsity via the horseshoe
C. M. Carvalho, N. G. Polson, and J. G. Scott · 2009
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Learning the structure of deep sparse graphical models
R. P. Adams, H. M. Wallach, and Z. Ghahramani · 2010
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Tracking body and hands for gesture recognition: Natops aircraft handling signals database
Y. Song, D. Demirdjian, and R. Davis · 2011
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Mean field variational Bayes for elaborate distributions
M. P. Wand, J. T. Ormerod, S. A. Padoan, R. Fuhrwirth, et al · 2011
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Stochastic gradient VB and the variational auto-encoder
D. P. Kingma and M. Welling · 2014
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Black box variational inference
R. Ranganath, S. Gerrish, and D. M. Blei · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Cited alongside, same era.
Doubly stochastic variational Bayes for non-conjugate inference
M. Titsias and M. Lázaro-gredilla · 2014
Cited alongside, same era.
Hamiltonian monte carlo for hierarchical models
M. Betancourt and M. Girolami · 2015
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Weight uncertainty in neural networks
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
Cited alongside, same era.
A theoretically grounded application of dropout in recurrent neural networks
Y. Gal and Z. Ghahramani · 2016
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Deep Bayesian active learning with image data
Y. Gal, R. Islam, and Z. Ghahramani · 2016
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Black-box alpha divergence minimization
J. Hernandez-Lobato, Y. Li, M. Rowland, T. Bui, D. Hernández-Lobato, and R. Turner · 2016
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Bayesian sparsity for intractable distributions
J. B. Ingraham and D. S. Marks · 2016
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Structured and efficient variational deep learning with matrix Gaussian posteriors
C. Louizos and M. Welling · 2016
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Automatic node selection for deep neural networks using group lasso regularization
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Probabilistic backpropagation for scalable learning of bayesian neural networks
J. M. Hernández-Lobato and R. P. Adams · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
D. P. Kingma, T. Salimans, and M. Welling · 2015
Cited alongside, same era.
Autograd: Effortless gradients in numpy
D. Maclaurin, D. Duvenaud, and R. P. Adams · 2015
Cited alongside, same era.
Auto-sizing neural networks: With applications to n-gram language models
K. Murray and D. Chiang · 2015
Cited alongside, same era.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
Cited alongside, same era.
T. Ochiai, S. Matsuda, H. Watanabe, and S. Katagiri · 2016
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Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
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Personalizing gesture recognition using hierarchical bayesian neural networks
A. Joshi, S. Ghosh, M. Betke, S. Sclaroff, and H. Pfister · 2017
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Variational dropout sparsifies deep neural networks
D. Molchanov, A. Ashukha, and D. Vetrov · 2017
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On the hyperprior choice for the global shrinkage parameter in the horseshoe prior
J. Piironen and A. Vehtari · 2017
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Group sparse regularization for deep neural networks
S. Scardapane, D. Comminiello, A. Hussain, and A. Uncini · 2017
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