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Obtaining reliable and accurate quantification of uncertainty estimates from deep neural networks is important in safety-critical applications.
A mathematical theory of communication
Claude E Shannon · 1948
Earlier work this paper cites.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
Earlier work this paper cites.
Elementary applied statistics
Linton G Freeman · 1965
Earlier work this paper cites.
Statistical Decision Theory and Bayesian Analysis
James O Berger · 1985
Earlier work this paper cites.
CRC standard probability and statistics tables and formulae
Stephen Kokoska and Daniel Zwillinger · 2000
Earlier work this paper cites.
The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich · 2006
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Earlier work this paper cites.
Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Approximate inference for the loss-calibrated bayesian
Simon Lacoste-Julien, Ferenc Huszár, and Zoubin Ghahramani · 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.
Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 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.
A unifying view on dataset shift in classification
Jose G Moreno-Torres, Troy Raeder, RocíO Alaiz-RodríGuez, Nitesh V Chawla, and Francisco Herrera · 2012
Earlier work this paper cites.
Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
Earlier work this paper cites.
Probabilistic machine learning and artificial intelligence
Zoubin Ghahramani · 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.
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The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets
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Yarin Gal and Zoubin Ghahramani · 2016
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Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling
Carlos Riquelme, George Tucker, and Jasper Snoek · 2018
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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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Modeling uncertainty by learning a hierarchy of deep neural connections
Raanan Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov, and Gal Novik · 2019
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’in-between’uncertainty in bayesian neural networks
Andrew YK Foong, Yingzhen Li, José Miguel Hernández-Lobato, and Richard E Turner · 2019
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Verified uncertainty calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
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Deep residual learning for image recognition
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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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
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On wasserstein two-sample testing and related families of nonparametric tests
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
Michael A Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, and Anh Nguyen · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D Sculley, Joshua Dillon, Jie Ren, and Zachary Nado · 2019
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Well-calibrated model uncertainty with temperature scaling for dropout variational inference
Max-Heinrich Laves, Sontje Ihler, Karl-Philipp Kortmann, and Tobias Ortmaier · 2019
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Uncertainty-aware audiovisual activity recognition using deep bayesian variational inference
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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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Radial bayesian neural networks: Beyond discrete support in large-scale bayesian deep learning
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