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Deep Learning methods are known to suffer from calibration issues: they typically produce over-confident estimates.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
Earlier work this paper cites.
Backpropagation Applied to Handwritten Zip Code Recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
A practical bayesian framework for backpropagation networks
D. J. C. MacKay · 1992
Earlier work this paper cites.
Early stopping-but when?
Lutz Prechelt · 1998
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
J. Platt · 1999
Earlier work this paper cites.
Information theory, inference and learning algorithms
David JC MacKay and David JC Mac Kay · 2003
Earlier work this paper cites.
Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Earlier work this paper cites.
Simple robust averages of forecasts: Some empirical results
Victor Richmond R Jose and Robert L Winkler · 2008
Earlier work this paper cites.
EyePACS: An Adaptable Telemedicine System for Diabetic Retinopathy Screening
Jorge Cuadros and George Bresnick · 2009
Earlier work this paper cites.
Cifar-10 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
Earlier work this paper cites.
Practical variational inference for neural networks
A. Graves · 2011
Earlier work this paper cites.
Bayesian learning for neural networks
Radford M Neal · 2012
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Weight uncertainty in neural networks, 2015
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Why m heads are better than one: Training a diverse ensemble of deep networks
Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David Crandall, and Dhruv Batra · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Dropout as a bayesian approximation
Y. Gal and Z. Ghahramani · 2016
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2017
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Entropy and mutual information in models of deep neural networks
Marylou Gabrié, Andre Manoel, Clément Luneau, Nicolas Macris, Florent Krzakala, Lenka Zdeborová, et al · 2018
Later among the works it cites.
Imagenette and Imagewoof
Jeremy Howard · 2018
Later among the works it cites.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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A mean field view of the landscape of two-layer neural networks
Song Mei, Andrea Montanari, and Phan-Minh Nguyen · 2018
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, et al · 2016
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Structured and efficient variational deep learning with matrix gaussian posteriors
C. Louizos and M. Welling · 2016
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Stochastic variational deep kernel learning
Andrew G Wilson, Zhiting Hu, Ruslan R Salakhutdinov, and Eric P Xing · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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On the inductive bias of neural tangent kernels
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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
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A simple baseline for bayesian uncertainty in deep learning
W. Maddox, T. Garipov, P. Izmailov, D. Vetrov, and A. G. Wilson · 2019
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Probability forecasts and their combination: A research perspective
Robert L. Winkler, Yael Grushka-Cockayne, Kenneth C. Lichtendahl, and Victor Richmond R. Jose · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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How good is the bayes posterior in deep neural networks really?
Florian Wenzel, Kevin Roth, Bastiaan S Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning, 2021
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Combining ensembles and data augmentation can harm your calibration, 2021
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Should ensemble members be calibrated?, 2021
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