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Bayesian Neural Networks (BNNs) place priors over the parameters in a neural network.
A practical bayesian framework for backpropagation networks
MacKay, D. J · 1992
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Practical variational inference for neural networks
Graves, A · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
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Nonparametric variational inference
Gershman, S., Hoffman, M., and Blei, D · 2012
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Bayesian learning for neural networks , volume 118
Neal, R. M · 2012
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Stochastic gradient hamiltonian monte carlo
Chen, T., Fox, E., and Guestrin, C · 2014
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Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Structured and efficient variational deep learning with matrix gaussian posteriors
Louizos, C. and Welling, M · 2016
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Myshkov, P. and Julier, S · 2016
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Hierarchical variational models
Ranganath, R., Tran, D., and Blei, D · 2016
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Multiplicative normalizing flows for variational bayesian neural networks
Louizos, C. and Welling, M · 2017
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Variational boosting: Iteratively refining posterior approximations
Miller, A. C., Foti, N. J., and Adams, R. P · 2017
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Implicit Weight Uncertainty in Neural Networks
Pawlowski, N., Brock, A., Lee, M. C. H., Rajchl, M., and Glocker, B · 2017
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Uncertainty in neural networks: Bayesian ensembling
Pearce, T., Zaki, M., Brintrup, A., and Neel, A · 2018
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Frequentist uncertainty estimates for deep learning
Tagasovska, N. and Lopez-Paz, D · 2018
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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An empirical evaluation of bayesian inference methods for bayesian neural networks
Zhao, R. and Ji, Q · 2018
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Functional variational bayesian neural networks
Sun, S., Zhang, G., Shi, J., and Grosse, R · 2019
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