Fetching the paper…
Reading the bibliography…
We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
Earlier work this paper cites.
On optimum recognition error and reject tradeoff
C Chow · 1970
Earlier work this paper cites.
A method for improving classification reliability of multilayer perceptrons
Luigi Pietro Cordella, Claudio De Stefano, Francesco Tortorella, and Mario Vento · 1995
Earlier work this paper cites.
Bagging predictors
Leo Breiman · 1996
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
Earlier work this paper cites.
To reject or not to reject: that is the question-an answer in case of neural classifiers
Claudio De Stefano, Carlo Sansone, and Mario Vento · 2000
Earlier work this paper cites.
Support vector machines with embedded reject option
Giorgio Fumera and Fabio Roli · 2002
Earlier work this paper cites.
Classification with a reject option using a hinge loss
Peter L Bartlett and Marten H Wegkamp · 2008
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 and Geoffrey Hinton · 2009
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2009
Cited alongside, same era.
On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Cited alongside, same era.
Agnostic selective classification
Yair Wiener and Ran El-Yaniv · 2011
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Later among the works it cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
Later among the works it cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Later among the works it cites.
Snapshot ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Cited alongside, same era.
Very deep convolutional neural network based image classification using small training sample size
Shuying Liu and Weihong Deng · 2015
Cited alongside, same era.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F Cooper, and Milos Hauskrecht · 2015
Cited alongside, same era.
Agnostic pointwise-competitive selective classification
Yair Wiener and Ran El-Yaniv · 2015
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Later among the works it cites.
Early stopping without a validation set
Maren Mahsereci, Lukas Balles, Christoph Lassner, and Philipp Hennig · 2017
Later among the works it cites.
Distance-based confidence score for neural network classifiers
Amit Mandelbaum and Daphna Weinshall · 2017
Later among the works it cites.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Closest in time.