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Bayesian inference promises to ground and improve the performance of deep neural networks.
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, and Max Welling · 2012
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Bayesian sampling using stochastic gradient thermostats
Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D Skeel, and Hartmut Neven · 2014
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Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Privacy for free: Posterior sampling and stochastic gradient monte carlo
Yu-Xiang Wang, Stephen Fienberg, and Alex Smola · 2015
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Bala Rajaratnam and Doug Sparks · 2015
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A complete recipe for stochastic gradient mcmc
Yi-An Ma, Tianqi Chen, and Emily Fox · 2015
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On the convergence of stochastic gradient mcmc algorithms with high-order integrators
Changyou Chen, Nan Ding, and Lawrence Carin · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
Cited alongside, same era.
Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Bayesian dark knowledge
Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
Cited alongside, same era.
Why is posterior sampling better than optimism for reinforcement learning?
Ian Osband and Benjamin Van Roy · 2017
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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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Stochastic gradient descent as approximate bayesian inference
Stephan Mandt, Matthew D Hoffman, and David M Blei · 2017
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Bayesian uncertainty estimation for batch normalized deep networks
Mattias Teye, Hossein Azizpour, and Kevin Smith · 2018
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Wngrad: learn the learning rate in gradient descent
Xiaoxia Wu, Rachel Ward, and Léon Bottou · 2018
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Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, and Sebastian J Vollmer · 2016
Cited alongside, same era.
Preconditioned stochastic gradient langevin dynamics for deep neural networks
Chunyuan Li, Changyou Chen, David Carlson, and Lawrence Carin · 2016
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Cited alongside, same era.
Hongyi Zhang, Yann N Dauphin, and Tengyu Ma · 2019
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Cyclical stochastic gradient mcmc for bayesian deep learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson · 2019
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A simple baseline for bayesian uncertainty in deep learning
Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, and Andrew Gordon Wilson · 2019
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