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Deep neural networks (DNNs) are poorly calibrated when trained in conventional ways.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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The elements of statistical learning , volume 1
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Practical variational inference for neural networks
Alex Graves · 2011
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Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Mai Nguyen, Jason Yosinski, and Jeff Clune · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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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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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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The malicious use of artificial intelligence: Forecasting, prevention, and mitigation
Miles Brundage, Shahar Avin, Jack Clark, Helen Toner, Peter Eckersley, Ben Garfinkel, Allan Dafoe, Paul Scharre, Thomas Zeitzoff, Bobby Filar, Hyrum S. Anderson, Heather Roff, Gregory C. Allen, Jacob Steinhardt, Carrick Flynn, Seán Ó hÉigeartaigh, Simon Beard, Haydn Belfield, Sebastian Farquhar, Clare Lyle, Rebecca Crootof, Owain Evans, Michael Page, Joanna Bryson, Roman Yampolskiy, and Dario Amodei · 2018
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To trust or not to trust a classifier
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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 learning , volume 1
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 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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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Heinrich Jiang, Been Kim, Melody Guan, and Maya Gupta · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
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Bayesian inference for large scale image classification
Jonathan Heek and Nal Kalchbrenner · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey Hinton · 2019
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Modeling uncertainty by learning a hierarchy of deep neural connections
Raanan Y Rohekar, Yaniv Gurwicz, Shami Nisimov, and Gal Novik · 2019
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
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Adaptive cross-modal few-shot learning
Chen Xing, Negar Rostamzadeh, Boris N Oreshkin, and Pedro O Pinheiro · 2019
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