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The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J. C · 1999
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Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B. and Elkan, C · 2002
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Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
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Pattern Recognition and Machine Learning
Bishop, C. M · 2006
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Learning multiple layers of features from tiny images, 2009
Krizhevsky, A. and Hinton, G · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Fast dropout training
Wang, S. and Manning, C · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Deepdriving: Learning affordance for direct perception in autonomous driving
Chen, C., Seff, A., Kornhauser, A., and Xiao, J · 2015
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Variational dropout and the local reparameterization trick
Kingma, D. P., Salimans, T., and Welling, M · 2015
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Obtaining Well Calibrated Probabilities Using Bayesian Binning
Naeini, M. P., Cooper, G. F., and Hauskrecht, M · 2015
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Uncertainty in Deep Learning
Gal, Y · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Categorical Reparameterization with Gumbel-Softmax
Jang, E., Gu, S., and Poole, B · 2016
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Multiplicative normalizing flows for variational bayesian neural networks
Louizos, C. and Welling, M · 2017
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Regularizing neural networks by penalizing confident output distributions
Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., and Hinton, G · 2017
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Accurate uncertainties for deep learning using calibrated regression
Kuleshov, V., Fenner, N., and Ermon, S · 2018
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q · 2017
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Concrete dropout
Gal, Y., Hron, J., and Kendall, A
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Lee, K., Lee, K., Lee, H., and Shin, J · 2018
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Bayesian uncertainty estimation for batch normalized deep networks
Teye, M., Azizpour, H., and Smith, K · 2018
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Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
Kull, M., Nieto, M. P., Kängsepp, M., Silva Filho, T., Song, H., and Flach, P · 2019
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A simple baseline for bayesian uncertainty in deep learning
Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
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Wat heb je gezegd? detecting out-of-distribution translations with variational transformers
Xiao, T. Z., Gomez, A. N., and Gal, Y · 2019
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