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Learning probability distributions on the weights of neural networks (NNs) has recently proven beneficial in many applications.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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A practical bayesian framework for backpropagation networks
David JC MacKay · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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A basis-kernel representation of orthogonal matrices
Xiaobai Sun and Christian Bischof · 1995
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
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Matrix Variate Distributions
Arjun K Gupta and Daya K Nagar · 1999
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Near-bayesian exploration in polynomial time
J Zico Kolter and Andrew Y Ng · 2009
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A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E Schapire · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
Lihong Li, Wei Chu, John Langford, and Xuanhui Wang · 2011
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Matrix Computations
Gene H Golub and Charles F Van Loan · 2012
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Rmsprop: Divide the gradient by a running average of its recent magnitude
Geoffrey E Hinton, Nitish Srivastava, and Kevin Swersky · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 2015
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Efficient thompson sampling for online matrix-factorization recommendation
Jaya Kawale, Hung H Bui, Branislav Kveton, Long Tran-Thanh, and Sanjay Chawla · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Stochastic expectation propagation
Yingzhen Li, José Miguel Hernández-Lobato, and Richard E Turner · 2015
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, et al · 2016
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Improving variational auto-encoders using householder flow
Jakub M Tomczak and Max Welling · 2016
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Towards unifying hamiltonian monte carlo and slice sampling
Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, and Lawrence Carin · 2016
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Particle optimization in stochastic gradient mcmc
Changyou Chen and Ruiyi Zhang · 2017
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Bayesian recurrent neural networks
Meire Fortunato, Charles Blundell, and Oriol Vinyals · 2017
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Assumed density filtering methods for learning bayesian neural networks
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Vime: Variational information maximizing exploration
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Stein variational gradient descent: A general purpose bayesian inference algorithm
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Structured and efficient variational deep learning with matrix gaussian posteriors
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Convergence rates for a class of estimators based on stein’s identity
Chris J Oates, Jon Cockayne, François-Xavier Briol, and Mark Girolami · 2016
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Scalable bayesian learning of recurrent neural networks for language modeling
Zhe Gan, Chunyuan Li, Changyou Chen, Yunchen Pu, Qinliang Su, and Lawrence Carin · 2017
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Stein variational gradient descent as gradient flow
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