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We investigate deep Bayesian neural networks with Gaussian weight priors and a class of ReLU-like nonlinearities.
Fonctions de repartition an dimensions et leurs marges
Sklar, M · 1959
Earlier work this paper cites.
Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition
Fukushima, K. and Miyake, S · 1982
Earlier work this paper cites.
A simple weight decay can improve generalization
Krogh, A. and Hertz, J · 1991
Earlier work this paper cites.
A practical Bayesian framework for backpropagation networks
MacKay, D · 1992
Earlier work this paper cites.
Bayesian training of backpropagation networks by the hybrid Monte Carlo method
Neal, R · 1992
Earlier work this paper cites.
Bayesian learning for neural networks
Neal, R · 1996
Earlier work this paper cites.
Regression shrinkage and selection via the Lasso
Tibshirani, R · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Rasmussen, C. and Williams, C · 2006
Earlier work this paper cites.
Kernel methods for deep learning
Cho, Y. and Saul, L · 2009
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
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Earlier work this paper cites.
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Saatci, Y. and Wilson, A · 2017
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Analytic expressions for probabilistic moments of PL-DNN with Gaussian input
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Lee, J., Sohl-Dickstein, J., Pennington, J., Novak, R., Schoenholz, S., and Bahri, Y · 2018
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