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Traditional training of deep classifiers yields overconfident models that are not reliable under dataset shift.
Analysis of confident-classifiers for out-of-distribution detection
Vernekar, S., Gaurav, A., Denouden, T., Phan, B., Abdelzad, V., Salay, R., and Czarnecki, K · 1904
Earlier work this paper cites.
Out-of-distribution detection in classifiers via generation
Vernekar, S., Gaurav, A., Abdelzad, V., Denouden, T., Salay, R., and Czarnecki, K · 1910
Earlier work this paper cites.
A practical bayesian framework for backpropagation networks
MacKay, D. J · 1992
Earlier work this paper cites.
A database for handwritten text recognition research
Hull, J. J · 1994
Earlier work this paper cites.
Priors for infinite networks
Neal, R. M · 1996
Earlier work this paper cites.
Modeling wine preferences by data mining from physicochemical properties
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., and Reis, J · 1998
Earlier work this paper cites.
MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
Earlier work this paper cites.
Mcmc using hamiltonian dynamics
Neal, R. M. et al · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Neal, R. M · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
Hoffman, M. D. and Gelman, A · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D. M · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., and Langs, G · 2017
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Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A · 2018
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Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 2018
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Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling
Riquelme, C., Tucker, G., and Snoek, J · 2018
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Building robust classifiers through generation of confident out of distribution examples
Sricharan, K. and Srivastava, A · 2018
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Rezende, D. J. and Mohamed, S · 2015
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Uncertainty in deep learning
Gal, Y · 2016
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Emnist: an extension of mnist to handwritten letters, 2017
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A · 2017
Cited alongside, same era.
Decomposition of uncertainty for active learning and reliable reinforcement learning in stochastic systems
Depeweg, S., Hernández-Lobato, J. M., Doshi-Velez, F., and Udluft, S · 2017
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Lee, K., Lee, H., Lee, K., and Shin, J · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks, 2017
Liang, S., Li, Y., and Srikant, R · 2017
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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S
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Pyro: Deep universal probabilistic programming
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N. D · 2019
Later among the works it cites.
Creativeai: Deep learning for graphics
Mitra, N. J., Kokkinos, I., Guerrero, P., Thuerey, N., Kim, V., and Guibas, L · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Bayesian batch active learning as sparse subset approximation
Pinsler, R., Gordon, J., Nalisnick, E., and Hernández-Lobato, J. M · 2019
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Normalizing flows for deep anomaly detection
Ryzhikov, A., Borisyak, M., Ustyuzhanin, A., and Derkach, D · 2019
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