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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
LeCun, Y., Denker, J. S., and Solla, S. A · 1990
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Bayesian back-propagation
Buntine, W. L. and Weigend, A. S · 1991
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A practical Bayesian framework for backpropagation networks
MacKay, D. J · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Optimal brain surgeon and general network pruning
Hassibi, B., Stork, D. G., and Wolff, G. J · 1993
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Bayesian learning via stochastic dynamics
Neal, R. M · 1993
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Reinforcement learning: An introduction , volume 1
Sutton, R. S. and Barto, A. G · 1998
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Occam’s razor
Rasmussen, C. E. and Ghahramani, Z · 2001
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A note on the evidence and bayesian occam’s razor
Murray, I. and Ghahramani, Z · 2005
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Handling sparsity via the horseshoe
Carvalho, C. M., Polson, N. G., and Scott, J. G · 2009
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Learning the structure of deep sparse graphical models
Adams, R. P., Wallach, H. M., and Ghahramani, Z · 2010
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Mean field variational Bayes for elaborate distributions
Wand, M. P., Ormerod, J. T., Padoan, S. A., Fuhrwirth, R., et al · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
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Stochastic gradient VB and the variational auto-encoder
Kingma, D. P. and Welling, M · 2014
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Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D. M · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Cited alongside, same era.
Doubly stochastic variational Bayes for non-conjugate inference
Titsias, M. and Lázaro-gredilla, M · 2014
Cited alongside, same era.
Hamiltonian monte carlo for hierarchical models
Betancourt, M. and Girolami, M · 2015
Cited alongside, same era.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Cited alongside, same era.
Preconditioned stochastic gradient langevin dynamics for deep neural networks
Li, C., Chen, C., Carlson, D. E., and Carin, L · 2016
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Structured and efficient variational deep learning with matrix Gaussian posteriors
Louizos, C. and Welling, M · 2016
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Automatic node selection for deep neural networks using group lasso regularization
Ochiai, T., Matsuda, S., Watanabe, H., and Katagiri, S · 2016
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Deep reinforcement learning with double q-learning
van Hasselt, H., Guez, A., and Silver, D · 2016
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Learning structured sparsity in deep neural networks
Wen, W., Wu, C., Wang, Y., Chen, Y., and Li, H · 2016
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Learning and policy search in stochastic dynamical systems with bayesian neural networks
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Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R. P · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
Kingma, D. P., Salimans, T., and Welling, M · 2015
Cited alongside, same era.
Autograd: Effortless gradients in numpy
Maclaurin, D., Duvenaud, D., and Adams, R. P · 2015
Cited alongside, same era.
Auto-sizing neural networks: With applications to n-gram language models
Murray, K. and Chiang, D · 2015
Cited alongside, same era.
Learning the number of neurons in deep networks
Alvarez, J. M. and Salzmann, M · 2016
Cited alongside, same era.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
Depeweg, S., Hernández-Lobato, J. M., Doshi-Velez, F., and Udluft, S · 2017
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Model selection in bayesian neural networks via horseshoe priors
Ghosh, S. and Doshi-Velez, F · 2017
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Robust and efficient transfer learning with hidden parameter markov decision processes
Killian, T. W., Daulton, S., Doshi-Velez, F., and Konidaris, G · 2017
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Bayesian compression for deep learning
Louizos, C., Ullrich, K., and Welling, M · 2017
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Variational dropout sparsifies deep neural networks
Molchanov, D., Ashukha, A., and Vetrov, D · 2017
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Structured bayesian pruning via log-normal multiplicative noise
Neklyudov, K., Molchanov, D., Ashukha, A., and Vetrov, D. P · 2017
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On the hyperprior choice for the global shrinkage parameter in the horseshoe prior
Piironen, J. and Vehtari, A · 2017
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Group sparse regularization for deep neural networks
Scardapane, S., Comminiello, D., Hussain, A., and Uncini, A · 2017
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Scalable model selection for belief networks
Song, Z., Muraoka, Y., Fujimaki, R., and Carin, L · 2017
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