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Ensembles of models have been empirically shown to improve predictive performance and to yield robust measures of uncertainty.
Ensemble methods in machine learning
Thomas G Dietterich · 2000
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Evaluating predictive uncertainty challenge
Joaquin Quinonero-Candela, Carl Edward Rasmussen, Fabian Sinz, Olivier Bousquet, and Bernhard Schölkopf · 2005
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
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Bayesian dark knowledge
Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Unifying distillation and privileged information
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, and Vladimir Vapnik · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Deep gaussian processes for regression using approximate expectation propagation
Thang Bui, Daniel Hernández-Lobato, Jose Hernandez-Lobato, Yingzhen Li, and Richard Turner · 2016
Cited alongside, same era.
Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
Cited alongside, same era.
Very deep multilingual convolutional neural networks for lvcsr
Tom Sercu, Christian Puhrsch, Brian Kingsbury, and Yann LeCun · 2016
Cited alongside, same era.
Large scale distributed neural network training through online distillation
Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E Dahl, and Geoffrey E Hinton · 2018
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Knowledge distillation by on-the-fly native ensemble
Xu Lan, Xiatian Zhu, and Shaogang Gong · 2018
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Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
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Collaborative learning for deep neural networks
Guocong Song and Wei Chai · 2018
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Deep mutual learning
Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu · 2018
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Decomposition of uncertainty for active learning and reliable reinforcement learning in stochastic systems
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2017
Cited alongside, same era.
UCI machine learning repository
Dheeru Dua and E Karra Taniskidou · 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.
Andrey Malinin, Bruno Mlodozeniec, and Mark Gales · 2019
Later among the works it cites.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Towards understanding knowledge distillation
Mary Phuong and Christoph Lampert · 2019
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