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Despite their theoretical appealingness, Bayesian neural networks (BNNs) are left behind in real-world adoption, mainly due to persistent concerns on their scalability, accessibility, and reliability.
A practical Bayesian framework for backpropagation networks
David JC MacKay · 1992
Earlier work this paper cites.
Bayesian Learning for Neural Networks
Radford M Neal · 1995
Earlier work this paper cites.
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Florian Wenzel, Kevin Roth, Bastiaan S Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin · 2002
Earlier work this paper cites.
Hyperparameter ensembles for robustness and uncertainty quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton · 2006
Earlier work this paper cites.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 2007
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
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Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
Earlier work this paper cites.
Learning face representation from scratch
Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li · 2014
Earlier work this paper cites.
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Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling · 2015
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.
Dropout as a bayesian approximation: appendix
Yarin Gal and Zoubin Ghahramani · 2015
Earlier work this paper cites.
Probabilistic backpropagation for scalable learning of Bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 2015
Earlier work this paper cites.
Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
Earlier work this paper cites.
Learning and policy search in stochastic dynamical systems with Bayesian neural networks
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2016
Earlier work this paper cites.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Qiang Liu and Dilin Wang · 2016
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Structured and efficient variational deep learning with matrix gaussian posteriors
Christos Louizos and Max Welling · 2016
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Frontal to profile face verification in the wild
Soumyadip Sengupta, Jun-Cheng Chen, Carlos Castillo, Vishal M Patel, Rama Chellappa, and David W Jacobs · 2016
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Lewis Smith and Yarin Gal · 2018
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Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse · 2018
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Deterministic variational inference for robust bayesian neural networks
Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E Turner, Jose Miguel Hernandez-Lobato, and Alexander L Gaunt · 2018
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Noisy natural gradient as variational inference
Guodong Zhang, Shengyang Sun, David Duvenaud, and Roger Grosse · 2018
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Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments
Tianyue Zheng and Weihong Deng · 2018
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Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, and Siegfried Wahl · 2017
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Gradient estimators for implicit models
Yingzhen Li and Richard E Turner · 2017
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Christos Louizos and Max Welling · 2017
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Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
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Practical deep learning with Bayesian principles
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E Turner, Rio Yokota, and Mohammad Emtiyaz Khan · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Functional variational Bayesian neural networks
Shengyang Sun, Guodong Zhang, Jiaxin Shi, and Roger Grosse · 2019
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Bayesian deep ensembles via the neural tangent kernel
Bobby He, Balaji Lakshminarayanan, and Yee Whye Teh · 2020
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