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Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong.
Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y · 2011
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Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2014
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Explaining and harnessing adversarial examples
Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, Anh, Yosinski, Jason, and Clune, Jeff · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, Karen and Zisserman, Andrew · 2015
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Turkergaze: Crowdsourcing saliency with webcam based eye tracking
Xu, Pingmei, Ehinger, Krista A, Zhang, Yinda, Finkelstein, Adam, Kulkarni, Sanjeev R, and Xiao, Jianxiong · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, Fisher, Seff, Ari, Zhang, Yinda, Song, Shuran, Funkhouser, Thomas, and Xiao, Jianxiong · 2015
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Concrete problems in AI safety
Amodei, Dario, Olah, Chris, Steinhardt, Jacob, Christiano, Paul, Schulman, John, and Mané, Dan · 2016
Cited alongside, same era.
Zagoruyko, Sergey and Komodakis, Nikos · 2016
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
Learning uncertainty in regression tasks by deep neural networks
Gurevich, Pavel and Stuke, Hannes · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, Dan and Gimpel, Kevin · 2017
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Densely connected convolutional networks
Huang, Gao, Liu, Zhuang, van der Maaten, Laurens, and Weinberger, Kilian Q · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, Alex and Gal, Yarin · 2017
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Lee, Kimin, Lee, Honglak, Lee, Kibok, and Shin, Jinwoo · 2018
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DeVries, Terrance and Taylor, Graham W · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, Chuan, Pleiss, Geoff, Sun, Yu, and Weinberger, Kilian Q · 2017
Cited alongside, same era.
Liang, Shiyu, Li, Yixuan, and Srikant, R · 2018
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