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Machine learning plays an increasingly significant role in many aspects of our lives (including medicine, transportation, security, justice and other domains), making the potential consequences of false predictions increasingly devastating.
Bootstrap methods: another look at the jackknife
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A practical bayesian framework for backpropagation networks
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Learning multiple layers of features from tiny images
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An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 2011
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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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Agnostic selective classification
Wiener, Y. and El-Yaniv, R · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Fast and accurate deep network learning by exponential linear units (elus)
Clevert, D.-A., Unterthiner, T., and Hochreiter, S · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Selective classification for deep neural networks
Geifman, Y. and El-Yaniv, R · 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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Distance-based confidence score for neural network classifiers
Mandelbaum, A. and Weinshall, D · 2017
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Confidence from invariance to image transformations
Bahat, Y. and Shakhnarovich, G · 2018
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Selective zero-shot classification with augmented attributes
Song, J., Shen, C., Lei, J., Zeng, A.-X., Ou, K., Tao, D., and Song, M · 2018
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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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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2016
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Aggregated residual transformations for deep neural networks. corr abs/1611.05431 (2016), 2016
Xie, S., Girshick, R. B., Dollár, P., Tu, Z., and He, K · 2016
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Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
On the foundations of noise-free selective classification
El-Yaniv, R. and Wiener, Y
Cited in the paper.
On the foundations of noise-free selective classification
El-Yaniv, R. and Wiener, Y
Cited in the paper.
Pytorch torchvision models
PyTorch
Cited in the paper.
Brunner, T., Diehl, F., Truong Le, M., and Knoll, A · 2019
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Pytorch playground
Chen, A · 2019
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Ding, Y., Liu, J., Xiong, J., and Shi, Y · 2019
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Bias-reduced uncertainty estimation for deep neural classifiers
Geifman, Y., Uziel, G., and El-Yaniv, R · 2019
Later among the works it cites.