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Many methods have been proposed to quantify the predictive uncertainty associated with the outputs of deep neural networks.
Bias and confidence in not quite large samples
John Tukey · 1958
Earlier work this paper cites.
The infinitesimal jackknife
Louis A Jaeckel · 1972
Earlier work this paper cites.
The jackknife estimate of variance
Bradley Efron and Charles Stein · 1981
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Residuals and influence in regression
R Dennis Cook and Sanford Weisberg · 1982
Earlier work this paper cites.
Bootstrap methods: another look at the jackknife
Bradley Efron · 1992
Earlier work this paper cites.
Bayesian methods for adaptive models
David JC MacKay · 1992
Earlier work this paper cites.
Fast exact multiplication by the hessian
Barak A Pearlmutter · 1994
Earlier work this paper cites.
Ensemble learning for multi-layer networks
David Barber and Christopher M Bishop · 1998
Earlier work this paper cites.
The mnist database of handwritten digits, 1998
Yann LeCun, Corinna Cortes, and Christopher JC Burges · 1998
Earlier work this paper cites.
The unscented kalman filter for nonlinear estimation
Eric A Wan and Rudolph Van Der Merwe · 2000
Earlier work this paper cites.
Pattern Recognition and Machine Learning
Christopher M. Bishop · 2006
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.
Probabilistic Graphical Models: Principles and Techniques
D. Koller and N. Friedman · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A Krizhevsky · 2009
Earlier work this paper cites.
Notmnist dataset
Yaroslav Bulatov · 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 Ng · 2011
Earlier work this paper cites.
5601 notes: The sandwich estimator, 2013
Charles J. Geyer · 2013
Earlier work this paper cites.
Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
Cited alongside, same era.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
Cited alongside, same era.
Second-order stochastic optimization in linear time
Naman Agarwal, Brian Bullins, and Elad Hazan · 2016
Cited alongside, same era.
Statistical inference for model parameters in stochastic gradient descent
Xi Chen, Jason D Lee, Xin T Tong, and Yichen Zhang · 2016
Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Later among the works it cites.
Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
Later among the works it cites.
A scalable laplace approximation for neural networks
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
Later among the works it cites.
Github code: Evidential deep learning to quantify classification uncertainty, 2018
Murat Sensoy · 2018
Later among the works it cites.
Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 2018
Later among the works it cites.
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Cited alongside, same era.
Jean Daunizeau · 2017
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Snapshot ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse · 2018
Later among the works it cites.
The relevance of bayesian layer positioning to model uncertainty in deep bayesian active learning
Jiaming Zeng, Adam Lesnikowski, and Jose M Alvarez · 2018
Later among the works it cites.
A higher-order swiss army infinitesimal jackknife
Ryan Giordano, Michael I Jordan, and Tamara Broderick · 2019
Later among the works it cites.
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
Later among the works it cites.
A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
Later among the works it cites.
Can you trust this prediction? auditing pointwise reliability after learning
Peter Schulam and Suchi Saria · 2019
Later among the works it cites.
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
Later among the works it cites.
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, and Dmitry Vetrov · 2020
Closest in time.
Fast predictive uncertainty for classification with bayesian deep networks, 2020
Marius Hobbhahn, Agustinus Kristiadi, and Philipp Hennig · 2020
Closest in time.
Being bayesian, even just a bit, fixes overconfidence in relu networks
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig · 2020
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How good is the bayes posterior in deep neural networks really?
Florian Wenzel, Kevin Roth, Bastiaan S Veeling, Jakub Światkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin · 2020
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