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Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large number of distribution parameters.
On the theory of the brownian motion
George E Uhlenbeck and Leonard S Ornstein · 1930
Earlier work this paper cites.
Monte carlo sampling methods using markov chains and their applications
WK Hastings · 1970
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Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
Earlier work this paper cites.
The evidence framework applied to classification networks
David JC MacKay · 1992
Earlier work this paper cites.
Probable networks and plausible predictions-a review of practical bayesian methods for supervised neural networks
David JC MacKay · 1995
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Markov chain Monte Carlo: stochastic simulation for Bayesian inference
Dani Gamerman and Hedibert F Lopes · 2006
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
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Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
Sungjin Ahn, Anoop Korattikara Balan, and Max Welling · 2012
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Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
Earlier work this paper cites.
Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
Earlier work this paper cites.
Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
Earlier work this paper cites.
On the number of response regions of deep feed forward networks with piece-wise linear activations
Razvan Pascanu, Guido Montufar, and Yoshua Bengio · 2013
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Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Variational bayesian inference algorithms for infinite relational model of network data
Takuya Konishi, Takatomi Kubo, Kazuho Watanabe, and Kazushi Ikeda · 2014
Earlier work this paper cites.
On the number of linear regions of deep neural networks
Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Reducing overfitting in deep networks by decorrelating representations
Michael Cogswell, Faruk Ahmed, Ross Girshick, Larry Zitnick, and Dhruv Batra · 2015
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Brody Huval, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Toki Migimatsu, Royce Cheng-Yue, et al · 2015
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Diederick P Kingma and Jimmy Ba · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Xiaoyang Huang, Jiancheng Yang, Linguo Li, Haoran Deng, Bingbing Ni, and Yi Xu · 2019
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Adv-BNN: Improved adversarial defense through robust bayesian neural network
Xuanqing Liu, Yao Li, Chongruo Wu, and Cho-Jui Hsieh · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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Privacy risks of securing machine learning models against adversarial examples
Liwei Song, Reza Shokri, and Prateek Mittal · 2019
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Variational inference: A review for statisticians
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