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Even before deep learning architectures became the de facto models for complex computer vision tasks, the softmax function was, given its elegant properties, already used to analyze the predictions of feedforward neural networks.
Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition
J. S. Bridle · 1990
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Pattern Recognition and Machine Learning
C. M. Bishop · 2006
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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The limitations of deep learning in adversarial settings
N. Papernot, P. D. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2015
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You only look once: Unified, real-time object detection
J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. A. Wagner · 2017
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On the properties of the softmax function with application in game theory and reinforcement learning
B. Gao and L. Pavel · 2017
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Towards evaluating the robustness of neural networks
N. Carlini and D. A. Wagner · 2016
Cited alongside, same era.
A. Athalye, N. Carlini, and D. Wagner · 2018
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