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Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space.
A practical Bayesian framework for backpropagation networks
D. J. MacKay · 1992
Earlier work this paper cites.
Bayesian learning via stochastic dynamics
R. M. Neal · 1993
Earlier work this paper cites.
Introduction to Gaussian processes
D. J. MacKay · 1998
Earlier work this paper cites.
Gaussian process latent variable models for visualisation of high dimensional data
N. D. Lawrence · 2004
Earlier work this paper cites.
A unifying view of sparse approximate Gaussian process regression
J. Quiñonero-Candela and C. E. Rasmussen · 2005
Earlier work this paper cites.
A hypercube-based encoding for evolving large-scale neural networks
K. O. Stanley, D. B. D’Ambrosio, and J. Gauci · 2009
Earlier work this paper cites.
Variational learning of inducing variables in sparse Gaussian processes
M. K. Titsias · 2009
Earlier work this paper cites.
Bayesian active learning for classification and preference learning
N. Houlsby, F. Huszár, Z. Ghahramani, and M. Lengyel · 2011
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
R. M. Neal · 2012
Earlier work this paper cites.
Gaussian processes for big data
J. Hensman, N. Fusi, and N. D. Lawrence · 2013
Earlier work this paper cites.
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D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
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Earlier work this paper cites.
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C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
Structured variational learning of Bayesian neural networks with horseshoe priors
S. Ghosh, J. Yao, and F. Doshi-Velez · 2018
Later among the works it cites.
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Expressive priors in Bayesian neural networks: Kernel combinations and periodic functions
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Functional variational Bayesian neural networks
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