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The reliable measurement of confidence in classifiers' predictions is very important for many applications and is, therefore, an important part of classifier design.
The condensed nearest neighbor rule (corresp.)
Hart, Peter · 1968
Earlier work this paper cites.
Bayesian methods for adaptive models
MacKay, David JC · 1992
Earlier work this paper cites.
A comparison of some error estimates for neural network models
Tibshirani, Robert · 1996
Earlier work this paper cites.
Confidence measures for neural network classifiers
Zaragoza, Hugo and d’Alché Buc, Florence · 1998
Earlier work this paper cites.
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
Bauer, Eric and Kohavi, Ron · 1999
Earlier work this paper cites.
Ensemble methods in machine learning
Dietterich, Thomas G · 2000
Earlier work this paper cites.
Novelty detection: a review—part 2:: neural network based approaches
Markou, Markos and Singh, Sameer · 2003
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Hadsell, Raia, Chopra, Sumit, and LeCun, Yann · 2006
Earlier work this paper cites.
Learning a nonlinear embedding by preserving class neighbourhood structure
Salakhutdinov, Ruslan and Hinton, Geoffrey E · 2007
Earlier work this paper cites.
Fast k nearest neighbor search using gpu
Garcia, Vincent, Debreuve, Eric, and Barlaud, Michel · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
Earlier work this paper cites.
Zero-shot learning with semantic output codes
Palatucci, Mark, Pomerleau, Dean, Hinton, Geoffrey E, and Mitchell, Tom M · 2009
Earlier work this paper cites.
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Coates, Adam, Lee, Honglak, and Ng, Andrew Y · 2010
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Gunadi, Hendra · 2011
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Cited alongside, same era.
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Neal, Radford M · 2012
Cited alongside, same era.
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Long-term recurrent convolutional networks for visual recognition and description
Donahue, Jeffrey, Anne Hendricks, Lisa, Guadarrama, Sergio, Rohrbach, Marcus, Venugopalan, Subhashini, Saenko, Kate, and Darrell, Trevor · 2015
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Hoffer, Elad and Ailon, Nir · 2015
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Hyvönen, Ville, Pitkänen, Teemu, Tasoulis, Sotiris, Jääsaari, Elias, Tuomainen, Risto, Wang, Liang, Corander, Jukka, and Roos, Teemu · 2015
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Schroff, Florian, Kalenichenko, Dmitry, and Philbin, James · 2015
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Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2013
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Hu, Hexiang, Zhou, Guang-Tong, Deng, Zhiwei, Liao, Zicheng, and Mori, Greg · 2016
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Deep networks with stochastic depth
Huang, Gao, Sun, Yu, Liu, Zhuang, Sedra, Daniel, and Weinberger, Kilian Q · 2016
Later among the works it cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, Balaji, Pritzel, Alexander, and Blundell, Charles · 2016
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Adversarial training methods for semi-supervised text classification
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Tadmor, Oren, Rosenwein, Tal, Shalev-Shwartz, Shai, Wexler, Yonatan, and Shashua, Amnon · 2016
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