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Existing Bayesian treatments of neural networks are typically characterized by weak prior and approximate posterior distributions according to which all the weights are drawn independently.
Keeping the neural networks simple by minimizing the description length of the weights
G. E. Hinton and D. Van Camp · 1993
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The helmholtz machine
P. Dayan, G. E. Hinton, R. M. Neal, and R. S. Zemel · 1995
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The" wake-sleep" algorithm for unsupervised neural networks
G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal · 1995
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Bayesian neural networks and density networks
D. J. MacKay · 1995
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Priors for infinite networks
R. M. Neal · 1996
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Gaussian process latent variable models for visualisation of high dimensional data
N. D. Lawrence · 2004
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A unifying view of sparse approximate gaussian process regression
J. Quiñonero-Candela and C. E. Rasmussen · 2005
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Sparse gaussian processes using pseudo-inputs
E. Snelson and Z. Ghahramani · 2006
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Compositional pattern producing networks: A novel abstraction of development
K. O. Stanley · 2007
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Hierarchical bayesian domain adaptation
J. R. Finkel and C. D. Manning · 2009
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Variational learning of inducing variables in sparse gaussian processes
M. Titsias · 2009
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Autonomous evolution of topographic regularities in artificial neural networks
J. Gauci and K. O. Stanley · 2010
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Learning programs: A hierarchical bayesian approach
P. Liang, M. I. Jordan, and D. Klein · 2010
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Bayesian gaussian process latent variable model
M. Titsias and N. D. Lawrence · 2010
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On the performance of indirect encoding across the continuum of regularity
J. Clune, K. O. Stanley, R. T. Pennock, and C. Ofria · 2011
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Learning a theory of causality
N. D. Goodman, T. D. Ullman, and J. B. Tenenbaum · 2011
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Bayesian learning for neural networks , volume 118
R. M. Neal · 2012
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An enhanced hypercube-based encoding for evolving the placement, density, and connectivity of neurons
S. Risi and K. O. Stanley · 2012
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Variational dropout and the local reparameterization trick
D. P. Kingma, T. Salimans, and M. Welling · 2015
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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Variational inference with normalizing flows
D. J. Rezende and S. Mohamed · 2015
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Local expectation gradients for black box variational inference
M. K. Titsias and M. Lázaro-Gredilla · 2015
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Density estimation using real nvp
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2016
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D. Krueger, C.-W. Huang, R. Islam, R. Turner, A. Lacoste, and A. Courville · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
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Multiplicative normalizing flows for variational bayesian neural networks
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Masked autoregressive flow for density estimation
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Implicit weight uncertainty in neural networks
N. Pawlowski, M. Rajchl, and B. Glocker · 2017
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The emergence of organizing structure in conceptual representation
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