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Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks.
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Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Jianzhong Wang · 2012
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 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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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Why m heads are better than one: Training a diverse ensemble of deep networks
Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David Crandall, and Dhruv Batra · 2015
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Uncertainty in deep learning
Yarin Gal · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Deep learning
Yoshua Bengio, Ian Goodfellow, and Aaron Courville · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Snapshot ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Concrete problems for autonomous vehicle safety: Advantages of bayesian deep learning
Rowan McAllister, Yarin Gal, Alex Kendall, Mark Van Der Wilk, Amar Shah, Roberto Cipolla, and Adrian Weller · 2017
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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Rethinking normalization and elimination singularity in neural networks
Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, and Alan Yuille · 2019
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Weight standardization
Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, and Alan Yuille · 2019
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, and Dmitry Vetrov · 2020
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Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Umang Bhatt, Yunfeng Zhang, Javier Antorán, Q Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Gauthier Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, et al · 2020
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Learning structured weight uncertainty in bayesian neural networks
Shengyang Sun, Changyou Chen, and Lawrence Carin · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Uncertainty estimation via stochastic batch normalization
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, and Dmitry Vetrov · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2018
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Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
Bertrand Charpentier, Daniel Zügner, and Stephan Günnemann · 2020
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Efficient and scalable bayesian neural nets with rank-1 factors
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, and Dustin Tran · 2020
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On the expressiveness of approximate inference in bayesian neural networks
Andrew YK Foong, David R Burt, Yingzhen Li, and Richard E Turner · 2020
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Tradi: Tracking deep neural network weight distributions
Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Séverine Dubuisson, and Isabelle Bloch · 2020
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Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M Dai, and Dustin Tran · 2020
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Being bayesian about categorical probability
Taejong Joo, Uijung Chung, and Min-Gwan Seo · 2020
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Hierarchical gaussian process priors for bayesian neural network weights
Theofanis Karaletsos and Thang D Bui · 2020
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Regression prior networks, 2020
Andrey Malinin, Sergey Chervontsev, Ivan Provilkov, and Mark Gales · 2020
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Pep: Parameter ensembling by perturbation
Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur, Demian Wassermann, and William Wells · 2020
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Structured weight priors for convolutional neural networks
Tim Pearce, Andrew YK Foong, and Alexandra Brintrup · 2020
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Uncertainty estimation using a single deep deterministic neural network
Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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Improving calibration of batchensemble with data augmentation
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, and Dustin Tran · 2020
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
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How good is the bayes posterior in deep neural networks really?
Florian Wenzel, Kevin Roth, Bastiaan S Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
Andrew Gordon Wilson and Pavel Izmailov · 2020
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Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell · 2020
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Bayesian neural network priors revisited
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard Turner, Mark van der Wilk, and Laurence Aitchison · 2021
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Second opinion needed: communicating uncertainty in medical machine learning
Benjamin Kompa, Jasper Snoek, and Andrew L Beam · 2021
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Dice: Diversity in deep ensembles via conditional redundancy adversarial estimation
Alexandre Rame and Matthieu Cord · 2021
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Information aware max-norm dirichlet networks for predictive uncertainty estimation
Theodoros Tsiligkaridis · 2021
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