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It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures.
Regression Quantiles
Koenker, R.; and Bassett, G., Jr. 1978 · 1978
Earlier work this paper cites.
A practical Bayesian framework for backpropagation networks
MacKay, D. J. 1992 · 1992
Earlier work this paper cites.
Robust out-of-distribution detection for neural networks
Chen, J.; Li, Y.; Wu, X.; Liang, Y.; and Jha, S. 2020 · 2003
Earlier work this paper cites.
van Amersfoort, J.; Smith, L.; Teh, Y. W.; and Gal, Y. 2020 · 2003
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Rasmussen, C. E.; and Williams, C. K. I. 2006 · 2006
Earlier work this paper cites.
Recurrent neural network based language model
Mikolov, T.; Karafiát, M.; Burget, L.; Cernockỳ, J.; and Khudanpur, S. 2010 · 2010
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics
Welling, M.; and Teh, Y. W. 2011 · 2011
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G.; Deng, L.; Yu, D.; Dahl, G. E.; Mohamed, A.-r.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T. N.; et al. 2012 · 2012
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Neal, R. M. 2012 · 2012
Earlier work this paper cites.
Bayesian and frequentist regression methods
Wakefield, J. 2013 · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
Earlier work this paper cites.
Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning
Alipanahi, B.; Delong, A.; Weirauch, M. T.; and Frey, B. J. 2015 · 2015
Earlier work this paper cites.
Weight Uncertainty in Neural Network
Blundell, C.; Cornebise, J.; Kavukcuoglu, K.; and Wierstra, D. 2015 · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
Cited alongside, same era.
End to end learning for self-driving cars
Bojarski, M.; Del Testa, D.; Dworakowski, D.; Firner, B.; Flepp, B.; Goyal, P.; Jackel, L. D.; Monfort, M.; Muller, U.; Zhang, J.; et al. 2016 · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y.; and Ghahramani, Z. 2016 · 2016
Cited alongside, same era.
Zagoruyko, S.; and Komodakis, N. 2016 · 2016
Cited alongside, same era.
Confidence scoring using whitebox meta-models with linear classifier probes
Chen, T.; Navrátil, J.; Iyengar, V.; and Shanmugam, K. 2019 · 2019
Later among the works it cites.
Model Selection in Bayesian Neural Networks via Horseshoe Priors
Ghosh, S.; Yao, J.; and Doshi-Velez, F. 2019 · 2019
Later among the works it cites.
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
Kull, M.; Perello Nieto, M.; Kängsepp, M.; Silva Filho, T.; Song, H.; and Flach, P. 2019 · 2019
Later among the works it cites.
Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness
Malinin, A.; and Gales, M. 2019 · 2019
Later among the works it cites.
Discriminative jackknife: Quantifying uncertainty in deep learning via higher-order influence functions
Alaa, A.; and Van Der Schaar, M. 2020 · 2020
Later among the works it cites.
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Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
Cited alongside, same era.
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Lakshminarayanan, B.; Pritzel, A.; and Blundell, C. 2017 · 2017
Cited alongside, same era.
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Litjens, G.; Kooi, T.; Bejnordi, B. E.; Setio, A. A. A.; Ciompi, F.; Ghafoorian, M.; Van Der Laak, J. A.; Van Ginneken, B.; and Sánchez, C. I. 2017 · 2017
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
Towards maximizing the representation gap between in-domain & out-of-distribution examples
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Later among the works it cites.
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Later among the works it cites.
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