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Ensemble methods which average over multiple neural network predictions are a simple approach to improve a model's calibration and robustness.
Measuring Calibration in Deep Learning
Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 1904
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The Comparison and Evaluation of Forecasters
Morris H. DeGroot and Stephen E. Fienberg · 1983
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Efficient estimations from a slowly convergent Robbins-Monro process
David Ruppert · 1988
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Neural network ensembles
Lars Kai Hansen and Péter Salamon · 1990
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When networks disagree: Ensemble methods for hybrid neural networks
Michael P. Perrone and Leon N. Cooper · 1992
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Neural network ensembles, cross validation, and active learning
Anders Krogh and Jesper Vedelsby · 1995
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Ensemble methods in machine learning
Thomas G. Dietterich · 2000
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann Dauphin, and David Lopez-Paz · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Obtaining Well Calibrated Probabilities Using Bayesian Binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
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FitNets: Hints for thin deep nets
Alejandro Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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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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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q. Weinberger · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
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.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Data interpolating prediction: Alternative interpretation of mixup
Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi, and Sosuke Kobayashi · 2019
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Evaluating model calibration in classification
Juozas Vaicenavicius, D. Widmann, Carl R. Andersson, F. Lindsten, J. Roll, and Thomas Bo Schön · 2019
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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
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Calibration tests in multi-class classification: A unifying framework
D. Widmann, F. Lindsten, and D. Zachariah · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Mixup as locally linear out-of-manifold regularization
Hongyu Guo, Yongyi Mao, and Richong Zhang · 2018
Cited alongside, same era.
Reliable uncertainty estimates in deep neural networks using noise contrastive priors
Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, and James Davidson · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Cited alongside, same era.
An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
Cited alongside, same era.
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse · 2018
Cited alongside, same era.
Adversarial mixup resynthesizers
Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, and Christopher Joseph Pal · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
Cited alongside, same era.
Later among the works it cites.
Estimating example difficulty using variance of gradients
Chirag Agarwal and Sara Hooker · 2020
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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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Hsin-Ping Chou, S. Chang, J. Pan, Wei Wei, and D. Juan · 2020
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Efficient and scalable Bayesian neural nets with rank-1 factors
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, and Dustin Tran · 2020
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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Varmixup: Exploiting the latent space for robust training and inference
Puneet Mangla, Vedant Singh, Shreyas Jayant Havaldar, and Vineeth N. Balasubramanian · 2020
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Mixup inference: Better exploiting mixup to defend adversarial attacks
Tianyu Pang, Kun Xu, and Jun Zhu · 2020
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Improving uncertainty estimates through the relationship with adversarial robustness
Yao Qin, Xuezhi Wang, Alex Beutel, and Ed Huai hsin Chi · 2020
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Uncertainty quantification and deep ensembles
Rahul Rahaman and Alexandre H Thiery · 2020
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Improved robustness to open set inputs via tempered mixup
Ryne Roady, T. Hayes, and Christopher Kanan · 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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Hyperparameter ensembles for robustness and uncertainty quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton · 2020
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Should ensemble members be calibrated?, 2021
Xixin Wu and Mark Gales · 2021
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