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When the cost of misclassifying a sample is high, it is useful to have an accurate estimate of uncertainty in the prediction for that sample.
Generalized linear models , volume 37
McCullagh, P. and Nelder, J. A · 1989
Earlier work this paper cites.
Gaussian processes for regression
Williams, C. K. and Rasmussen, C. E · 1996
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J. et al · 1999
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
Earlier work this paper cites.
Pattern Recognition and Machine Learning (Information Science and Statistics)
Bishop, C. M · 2006
Earlier work this paper cites.
Robust fusion: Extreme value theory for recognition score normalization
Scheirer, W., Rocha, A., Micheals, R., and Boult, T · 2010
Earlier work this paper cites.
Toward open set recognition
Scheirer, W. J., de Rezende Rocha, A., Sapkota, A., and Boult, T. E · 2013
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
Earlier work this paper cites.
Probability models for open set recognition
Scheirer, W. J., Jain, L. P., and Boult, T. E · 2014
Earlier work this paper cites.
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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Dropout as a bayesian approximation
Gal, Y. and Ghahramani, Z · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B · 2015
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 2015
Towards open set deep networks
Bendale, A. and Boult, T. E · 2016
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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Fitted learning: Models with awareness of their limits
Kardan, N. and Stanley, K. O · 2016
Later among the works it cites.
Emnist: an extension of mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A · 2017
Later among the works it cites.
A survey of stealth malware: Attacks, mitigation measures, and steps toward autonomous open world solutions
Rudd, E., Rozsa, A., Gunther, M., and Boult, T · 2017
Later among the works it cites.
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Deep neural network based malware detection using two dimensional binary program features
Saxe, J. and Berlin, K · 2015
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A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Garnelo, M., Rosenbaum, D., Maddison, C. J., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D. J., and Eslami, S
Cited in the paper.
Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D. J., Eslami, S., and Teh, Y. W
Cited in the paper.
Meade: Towards a malicious email attachment detection engine
Rudd, E. M., Harang, R., and Saxe, J
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The extreme value machine
Rudd, E. M., Jain, L. P., Scheirer, W. J., and Boult, T. E
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To trust or not to trust a classifier
Jiang, H., Kim, B., and Gupta, M · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2018
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