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The great performances of deep learning are undeniable, with impressive results over a wide range of tasks.
Bayesian methods for adaptive models
MacKay, D. J · 1992
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.
The elements of statistical learning , volume 1
Friedman, J., Hastie, T., and Tibshirani, R · 2001
Earlier work this paper cites.
Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Zadrozny, B. and Elkan, C · 2001
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B. and Elkan, C · 2002
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Calibrating predictive model estimates to support personalized medicine
Jiang, X., Osl, M., Kim, J., and Ohno-Machado, L · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Y Ng, A · 2011
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Neal, R. M · 2012
Cited alongside, same era.
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-r., and Hinton, G · 2013
Cited alongside, same era.
Densenet: Implementing efficient convnet descriptor pyramids
Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., and Keutzer, K · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Cited alongside, same era.
Chest pathology detection using deep learning with non-medical training
Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., and Greenspan, H · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Later among the works it cites.
Structured and efficient variational deep learning with matrix gaussian posteriors
Louizos, C. and Welling, M · 2016
Later among the works it cites.
Zagoruyko, S. and Komodakis, N · 2016
Later among the works it cites.
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N. K., Kittler, H., and Halpern, A · 2017
Later among the works it cites.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Cited alongside, same era.
Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P., Cooper, G., and Hauskrecht, M · 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
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al
Cited in the paper.
Mnist dataset, 1998b
LeCun, Y., Cortes, C., and Burges, C
Cited in the paper.
Later among the works it cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Later among the works it cites.
Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2017
Later among the works it cites.
Fast and scalable bayesian deep learning by weight-perturbation in adam
Khan, M. E., Nielsen, D., Tangkaratt, V., Lin, W., Gal, Y., and Srivastava, A · 2018
Later among the works it cites.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C., and Kittler, H · 2018
Later among the works it cites.