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We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks.
Bayesian neural networks and density networks
David J.C. MacKay · 1994
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Radford M. Neal · 1996
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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MADE: masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
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Probabilistic backpropagation for scalable learning of Bayesian neural networks
Jose Miguel Hernandez-Lobato and Ryan Adams · 2015
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Variational Dropout and the Local Reparameterization Trick
Diederik. P. Kingma, T. Salimans, and M. Welling · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Markov chain Monte Carlo and variational inference: Bridging the gap
Tim Salimans, Diederik P. Kingma, and Max Welling · 2015
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Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Diederik P. Kingma · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
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